# Design and Control of a Buck-Boost DC-DC Power ```Design and Control of a Buck-Boost
DC-DC Power Converter
Robin Vujanic
Semester Thesis
July 2008
Supervision:
Dr. S. Mariethoz
Prof. M. Morari
Contents
1
2
3
Introduction
1.1 Motivation and Objectives
1.2 Converter Topology . . . .
1.3 Synchronous Rectification
1.4 Degrees of Freedom . . . .
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Power Losses
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Losses Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Modeling Power Losses . . . . . . . . . . . . . . . . . . . . . . . . . .
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Models of the Circuit
2.1 What is considered and what is not considered . .
2.2 Simulations in PLECS . . . . . . . . . . . . . . . . .
2.3 Basic Analytical Models: Full-Buck and Full-Boost
2.3.1 Buck Model . . . . . . . . . . . . . . . . . .
2.3.2 Boost Model . . . . . . . . . . . . . . . . . .
2.4 Buck-Boost Model . . . . . . . . . . . . . . . . . . .
2.4.1 Estimation Equations . . . . . . . . . . . . .
2.4.2 Equations for the four States . . . . . . . .
2.4.3 Equations for the State-Times . . . . . . . .
2.4.4 The Averaged Model . . . . . . . . . . . . .
2.4.5 The Hybrid Model . . . . . . . . . . . . . .
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Contents
4
5
Controller Design
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Why MPC . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Basic Control Strategies . . . . . . . . . . . . . . . . . . .
4.3.1 Buck, the Simplest Mode . . . . . . . . . . . . .
4.3.2 Boost Mode and the Gain Scheduling Technique
4.4 Buck-Boost Operation . . . . . . . . . . . . . . . . . . .
4.4.1 The Feedforward . . . . . . . . . . . . . . . . . .
4.4.2 The Feedback . . . . . . . . . . . . . . . . . . . .
4.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . .
Conclusion
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Bibliography
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iv
Chapter 1
Introduction
In Zweifelsfaellen entscheide man sich fuer das Richtige.
Karl Kraus
Abstract
A study on the properties and control of a promising circuit topology for a DC-DC buckboost power converter is presented. The circuit contains four transistors operated synchronously in couples. We propose a set of mathematical models to describe this circuit,
and an approach to determine the behavior of the losses occurring inside of it. These are
then combined in order to achieve a control scheme that drives the circuit while minimizing said losses. The control strategy proposed here is based on a combined feedback
(MPC) and feedforward action. Control performance parameters such as disturbances
rejection capability have been investigated as well.
1.1
T
Motivation and Objectives
he present work deals with the design and control implementation of a BuckBoost DC-DC power converter.
DC-DC power converters are employed in order to transform an unregulated DC
voltage input (i.e. a voltage that possibly contains disturbances) in a regulated output voltage. For example, a DC-DC power converter can transform an unregulated
(i.e. distorted) 9V input voltage in a regulated (i.e. ”clean”) voltage of 12V at the output. Some DC-DC power converters have a fixed output reference and ensure that
such voltage is always delivered, no matter what the input is; some others can have
a variable output reference, which can be therefore set depending on the current
need of the device the power converter is used in. The converter discussed in this
work belongs to this second category. In particular, the converter is able to deliver
output voltages both higher as well as lower than (or even equal to) the input voltage; this is why it is referred to as a ”buck-boost” (or ”step-up/step-down”) power
1
CHAPTER 1. INTRODUCTION
unregulated
v in
DC
DC
regulated
vout
Figure 1.1: Abstract functionality of a DC-DC Power Converter: an unregulated input voltage
is transformed into a regulated (i.e. ”clean”) output voltage; The output reference voltage, in
the case of a buck-boost converter, can be set to be either smaller, larger or equal the input
voltage.
converter. A schematic depicting the abstract functionality of a DC-DC power converter can be seen in Figure 1.1.
Such power converters are needed in a vast number of electrical devices, which
on one side is a motivation for this project, and on the other side also explains why
much research is still conducted on this topic. Very high efficiencies have been
achieved by converters currently on the market, yet the main goal of this project is to
push the everlasting quest for still-higher-efficiencies a step further, by exploiting a
novel converter topology and modern control techniques together with appropriate
design choices.
Recent studies on this topic (Frehner 2007) were able to establish some rudimental model of the circuit (the plant) and then applied standard control techniques
(PID and LQR based control schemes). These studies resulted in poor overall performance since:
• The topology was not exploited to its fullest; it was studied and controlled
only either in its full-buck mode or in its full-boost mode (see Chapter 2 for
what is meant with ”full-buck”, ”full-boost” modes), thereby simplifying the
analysis but causing unsatisfactory results as well.
• The implementation of these standard control techniques presented challenges,
mainly because of two reasons:
– first, with PID and LQR controls it is difficult to implement state and inputs constraints (which arise in a natural way when dealing with the control of this plant); an approach via saturators was attempted, but still the
controller performance was unsatisfactory, especially in the boost mode;
– second, especially with the PID approach the control of a multiple-inputs
plant becomes considerably more challenging compared to a single-input
plant; since the plant was considered always either in its full-boost or in
it full-buck mode, this problem has not actually been faced
2
1.2. CONVERTER TOPOLOGY
input stage
“buck”-leg
“boost”-leg
T1
output stage
T3
C
RL
L
Vin
T2
+
Vout
T4
RC
-
Figure 1.2: Schematic of the circuit topology used to achieve DC-DC power conversion; the
different stages are depicted, and notice the load, which is simulated by a current source for
the moment. Later, it will be replaced by a Load resistance for calculations.
• The controllers did not account for the losses inside the circuit, nor a model
for these losses was established.
The present work shows the advances made with respect to these limitations.
1.2
Converter Topology
onsider Figure 1.2, which depicts the circuit topology employed for this project.
It can be split into two separated voltage conversion stages, the ”buck”-leg and
the ”boost”-leg; the first stage (the ”buck”-leg) consists of switches T1 and T2, while
the second stage (the ”boost”-leg) contains switches T3 and T4. We divide the circuit
in these two legs because it turns out that for proper functionality of the circuit
(i.e. in order to avoid a voltage source to be short-circuited - a condition referred
to as ”shoot-through”) each of these two pairs of switches (i.e. for example T1 and
T2) need to work in a complementary manner, and is therefore to be regarded as a
”couple” of switches. Switch T3 and T4 are the other ”couple of transistors” and
these two couples constitute the two circuit legs1 .
For T1 and T2 to work in a complementary manner means that when switch T1
is conducting current (it is on), switch T2 must be off, and vice-versa.
C
1 The
transistors used in this work for switching are MOSFETs.
3
CHAPTER 1. INTRODUCTION
d1
a)
1-d1
b)
d2
phase
Ts
Figure 1.3: Typical gate signals obtained for a certain choice of (d1 , d2 , φ); (a) depicts the
signals sent to the first couple of switches (T1 and T2) while (b) shows the signals sent to T3
and T4 (also notice the phase there).
On an important side note, in order to safely avoid the occurrence of both switches
being on (which would lead to shoot-through), short periods of time between the
switchings are introduced, during which both transistors are turned off; these are
so-called dead times2 . This way of operating switches for current rectification is
referred to as synchronous rectification and is briefly discussed in Section 1.3.
Getting back to the discussion on the active times of the switches, the following
naming convention has been used (see Figure 1.3): the time during which T1 is on
(and therefore T2 is off) is denoted as d1 . Since all of the switches are operated in a
cyclic manner (as it is usual in power converter designs), d1 is actually the portion of
time over a cycle during which T1 is on. To make an example, d1 = 0.2 means that
T1 is on 20% of the time of an entire cycle, and is usually referred to as the dutycycle of T1. Therefore, if negelecting small dead-times, 1 − d1 is the time portion
during which T2 is on (i.e. the ”duty-cycle” of T2). The same principle applies for
the second leg, where the portion of time during which T3 is on is denoted as d2
(and therefore 1 − d2 is the time during which T4 is on).
Further, notice that the two duty cycles d1 and d2 can be shifted one relative to
2 During these times, if necessary, conduction is ensured by the body-diodes found in parallel with the
transistors, which are actually a ”parasitic” result of the transistor’s fabrication.
4
1.3. SYNCHRONOUS RECTIFICATION
the other by adding a phase between them, which is denoted with φ 3 . These duty
cycles (d1 and d2 ) and the phase are important variables affecting the behavior of the
circuit because they are in fact the variables that are going to be controlled in order
to achieve the desired output voltage signal. Figure 1.3 shows the typical look of the
gate signals corresponding to certain choices of d1 , d2 and phase and illustrates the
Note: especially when considering the circuit in its ”pure” boost version, this
choice for d2 is *not* consistent with the literature where d2 would be the time during which T3 is off. This controvert convention was adopted at the beginning of this
study and was kept later on for compatibility with initial results.
For the rest of the circuit, the names used for the components is to be understood
as follows:
• L, C, Rload , RC , RL : these are the components inside the circuit. L and C are the
inductor and the (output) capacitor, Rload is the resistor used to model a load
on the output, RC and RL are the parasitic ESRs (Equivalent Series Resistances)
to L and C.
Note: unfortunately, C is also the de-facto established symbol for a matrix in
the standard matrix form of the state-space description of systems, and this
convention for naming this matrix is also used here (see below). However, the
meaning of each instance of C is clear from the context.
• vin , vout , vC , iL : input and output voltages, voltage across the capacitor, current through the inductor.
Note: in order to avoid confusion, voltages are going to be denoted with the
letter ”v” and inputs (to the plant, see Chapter 4) with the letter ”u”.
• T1, T2, T3, T4: the switching transistors.
1.3
Synchronous Rectification
W
hile earlier DC-DC power converters relied on the use of diodes for current
rectification (which is necessary for the converter’s operation), increased performances have been achieved by adopting synchronous rectification in the design
of the power supply instead. Synchronous rectification means that the functionality
3 For
readability of the code developed for this project, it is to be noted that a unit-less denotation has
been used for the phase. A phase of 0.3 means that the d2 signal is shifted of 30% of a duty cycle with
respect to the d1 signal, i.e. the switch T3 is going to switch in the on mode 30% of a cycle later than
when the switch T1 was turned on.
5
CHAPTER 1. INTRODUCTION
once provided by the diode - i.e. current rectification - is now undertaken by a rectifying transistor, typically a MOSFET. Such rectification improves efficiency, thermal
performance, power densities, manufacturability, reliability as well as having typically faster switching transients, and decreases the overall system cost for power
supplies (Selders 2003). These performance increases are mainly due to the fact that
the on-resistance of MOSFETs, RDS,on , can be reduced either by increasing the size
of the die or by paralleling discrete devices, while the forward voltage-drop across
diodes cannot be lowered under a certain (physically imposed) limit; this motivates
the choice of using synchronous rectifiers in the circuit topology studied for this
project.
1.4
Degrees of Freedom
sing the models that are going to be discussed in detail in Chapter 2, it is possible to obtain the vout /vin ratio provided by the circuit with a given combination
of d1 , d2 and φ.
Because of their definition, the d1 , d2 and φ variables are constrained to lie in the
interval [0,1], i.e.:
U
(d1 , d2 , φ) ∈ [0, 1]3
(1.1)
Two typical surfaces showing how the vout /vin ratio behaves with respect to the
(d1 , d2 , φ) variables at steady state are depicted in Figures 1.5 a) and b).
It is confirmed from this surfaces that the general behavior of the output voltage,
in the ideal case, follows the equation:
vout = vin
d1
d2
(1.2)
Some important aspects emerging from this figure have to be highlighted:
• First, notice how this confirms the choice of naming ”buck” and ”boost” the
two legs composing the converter made previously; in fact, d1 (the duty cycle
corresponding to the ”buck” leg) is a multiplicative factor to vin between zero
and one, while d2 (duty cycle of the ”boost” leg) is a multiplicative factor to
vin between one and .
• Second, this figure also shows that the output voltage varies linearly with
varying d1 while instead, when varying d2 , it varies non-linearly. The nonlinearity of the boost stage will have deep consequences for the development
of the control (it is going to pose a major challenge for the design of a valid
controller); this is going to be discussed more in detail in Chapter 2 and 4.
6
1.4. DEGREES OF FREEDOM
v
/ v = f(d1,d2)
out
in
7
6
vout / vin
5
4
3
2
1
0
1
d1
0.5
0
0.1
0.3
0.2
0.5
0.4
0.6
0.8
0.7
0.9
1
d2
dependence of the output voltage on d1 , d2
vout /vin: 0.4
1
0.9
0.8
d2
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.2
0.4
phase
0.6
0.8
1
0
0.1
0.2
d1
0.3
dependence of the output voltage on the phase φ
Figure 1.4: Dependence of the output voltage from the inputs of the plant, i.e. (d1 , d2 , φ)
7
0.4
CHAPTER 1. INTRODUCTION
0.7
0.
9
9
0.
1
1.2
1.4
1.6
0.3
0.2
1.8
9
0.
0.7
0.4
0.8
0.5
0.5
0.6
0.4
0.1
0.6
0.05
d2
0.8
0.5
1.2
0.7
2
1
1.4
0.6
1.2
1.6
2.4
0.4
1.8
2
3
0.1
0.8
0.
9
0.1
0.2
0.3
0.5
0.2
0.8
0.3
0.8
0.3
0.7
0.6
0.4
1
0.2
0.05
0.9
0.1
vout / v in (d1,d2) iso-lines
1
1.2
0.2
1.4
1.6
1.8
2
0.3
2.4
0.4
0.5
d1
3.4
3
0.6
4
0.7
0.8
3.4
0.9
4
5
6
Figure 1.5: Output-voltage iso-lines resulting from slicing Figure a) with planes perpendicular to the vertical axis. Notice that all the points along each of these curves represents d1 ,d2
combinations that will always deliver the same vout /vin ratio.
• Third, notice from Figure 1.5 b how the vout /vin ratio does not depend on
the phase φ; and while the vout /vin ratio is unaffected by changes in φ, variables describing the circuit state (in particular the inductor’s current iL ) are
indeed going to be affected by changes in the phase, resulting in different performances in terms of losses. This is a very important aspect, and leads to our
first ”degree of freedom”: the phase4 .
• Fourth, ”slicing” this surface along planes perpendicular to the vout /vin (”z”)axis results in the set of curves depicted in Figure 1.5. This is the set of curves
of constant output voltage as a function of d1 and d2 . This is also a result of
fundamental importance because it leads to our second ”degree of freedom”:
each choice of d1 and d2 along one of these curves will always lead to the same
output voltage.
The main objective of this project can now be stated as follows: by exploiting
these two degrees of freedom we will be able to affect the state of the circuit;
thus, many internal states will lead to the same output voltage, and the main task
will be to choose among all these possibilities the one that will lead to the least
possible power losses - i.e. to the most efficient way of driving the circuit.
This has been achieved as follows: first, different models of the circuit have been
4 The wording ”degree of freedom” is used to highlight the fact that φ can be chosen arbitrarily while
leaving the output voltage unaffected.
8
1.4. DEGREES OF FREEDOM
developed for different purposes, see next Chapter. After that, a thorough study of
the losses inside the circuit has been conducted using some of these models; this is
exposed in Chapter 3. Based on the study of the losses, the design of a control that
drives the circuit while accounting for losses has been done, and will be presented
in Chapter 4. Chapter 5 is a conclusive chapter, where possible outlooks will be
discussed and a summary of this project will be given.
9
Chapter 2
Models of the Circuit
Bildung ist das, was uebrig bleibt, wenn der letzte Dollar
weg ist.
Mark Twain
2.1
What is considered and what is not considered
T
he circuit being considered in this work is depicted in Figure 2.1. Notice that for
the development of the models, the output load is simulated with a resistance,
Rload . The meaning of the rest of the symbols has been exposed in Section 1.2.
In order to develop a controller, tools in order to forecast how the behavior of the
plant and the losses are going to be are necessary. In this work, two such tools have
been used:
1. Simulations in PLECS, a Simulink toolbox1 .
1 Part
of the validation of the losses behavior discussed later has also been conducted with PSpice, see
(Walter 2008)
T1
T3
C
L
Vin
T2
RL
+
T4
RC
-
Figure 2.1: Schematic of the circuit topology used to achieve the equations of the analytical
models.
11
CHAPTER 2. MODELS OF THE CIRCUIT
2. Analytical models of the circuit.
Under ”behavior of the circuit” we understand the following: we are interested
in knowing the currents inside the circuit (especially through the inductor) and the
voltages across the components (especially on the capacitor) as a function of the time
and with respect to changes to the input. Therefore this is the information our tools
should return us.
This translates into the following model structure for the plant:
• The states are the current through the inductor iL and the voltage across the
capacitor vC .
• Two of the inputs are the duty cycles that are imposed on the two pairs of
switches (one duty cycle for the input leg, one duty cycle for the output leg),
and a third input is the phase between the two duty cycles. As explained
previously, only one duty cycle is necessary in order to control two switches
because they must work in a complementary manner.
• The output is the output voltage (equal to vC in the ideal case - i.e. no ESR).
u
d1
d2
phase
Converter’s Circuit
(plant)
iL,vC
vout
(states)
Figure 2.2: Block diagram for the plant, depicting the inputs, the states and the output.
What is not going to be considered (at least for the formalization of the analytical
models) are the following non-idealities:
• Dead-times: they are implemented in the PLECS simulations but not in the
analytical models (see Section 2.3-2.4).
• Parasitic components: a number of parasitic and non-ideality-induced components could be added to the scheme, including various inductances and
capacitances, especially if a more detailed small- and large-signal description
for the transistors was to be made (rather than taking its ideal behavior, as has
been done). This is avoided here, these effects are actually present and will
deviate the results of an actual implementation from the simulation’s results.
12
2.2. SIMULATIONS IN PLECS
But the essential behaviors discussed in this work are still going to be present
and are still going to be important aspects for design decisions.
The only parasitic components considered are going to be RL and RC , mainly
due to backwards compatibility with previous works (Frehner 2007).
Reverse recovery and voltage drop of diodes, skin and proximity effects of inductors, transistors’ channel resistances and drain capacitance charge/discharge
mechanisms are some of the processes which are not modeled. Including them
in an analytical model greatly increases complexity (basically each additional
capacitance or inductance adds one more state to the model), and, further,
their presence influences homogenously the total losses rather than increasing
one in particular between the conduction or the switching losses (see Chapter
3). Since we are interested in a comparison between these tzpe of losses rather
than in their total amount, and since the stress for this study has been put on
simplicity and generality, these effects have been neglected.
• Discontinuous conduction mode is not considered.
In the following sub chapters, the two methodologies (PLECS Simulations and
Analytical Models) are described more in detail.
2.2
Simulations in PLECS
T
hese simulations have been used extensively at the beginning in order to gain
an understanding of the general behavior of the circuit without having to dwelve
too much into differential equations. The Simulink schematic used for these purposes is shown in Figure 2.3.
What has been done in this case is the following: first, for
D1 = {d1 ∈ R | 0 ≤ d1 ≤ 1}
D2 = {d2 ∈ R | 0 ≤ d2 ≤ 1}
P HASE = {φ ∈ R | 0 ≤ phase ≤ 1}
The inputs set
U := D1 &times; D2 &times; P HASE
has been ”gridded”, resulting in U ∗ ⊂ U . Then, for each knot of the discretized set
U ∗ ⊂ U , the behavior of the circuit has been studied.
The results of a single simulation done with PLECS can be seen in Figure 2.2,
where the predicted inductor current is depicted. As can be seen, this prediction
13
CHAPTER 2. MODELS OF THE CIRCUIT
Figure 2.3: Screenshot of the PLECS circuit used to make simulations.
indeed corresponds closely to the result we would have by applying an analytical
model (discussed later in this Chapter) instead.
In general, PLECS simulations have been useful in order to gain a general understanding of the circuit (for example, how does the vout /vin = f (d1 , d2 ) surface
looks like?) and of the losses. However, for decisional purposes they have been
completely substituted with simulations based on analytical models because of a
number of shortcomings this approach presents:
1. First, simulation times are much longer: this approach can increase calculation
times by hundreds. On one side, this makes evaluation of changes of circuit
behaviour with respect to small changes in components, control strategies etc,
less viable. And on the other hand, this forces the use of a wider grid for
the discretized set U ∗. Larger grid translates into less accurate evaluation of
optimal input trajectories (optimal with respect to losses, see Chapter 3).
2. Second, PLECS simulations have transients at the beginning. They can be
shortened by strategically supplying adequate initial conditions for the inductor’s current and the capacitor’s voltage (see Equations 2.30 - 2.35), but they
can never be completely avoided. Since the behavior of the circuit is considered at steady state, this results in even higher calculation times, because the
simulation time needs to be increased. Further, this makes the calculation of
RMS values of steady state signals less precise because there is no guarantee
that the chunk of signal taken in order to calculate the RMS value is already
completely at steady state, without having to resort to unfeasibly long simula14
2.2. SIMULATIONS IN PLECS
PLECS Simulation
30.24
30.22
iL(A)
30.2
30.18
30.16
30.14
30.12
30.1
2.97
2.971
2.972
2.973
2.974
2.975
2.976
time (s)
2.977
2.978
2.979
2.98
-3
x 10
result with PLECS simulations
Analytical Model Simulation
30.26
30.24
i L(A)
30.22
30.2
30.18
30.16
30.14
30.12
0
0.1
0.2
0.3
0.4
0.5
time (s)
0.6
0.7
0.8
0.9
1
-5
x 10
result with analytical models
Figure 2.4: Comparison between the result for the inductor’s current iL of a PLECS simulation with the prediction of the analytical model.
tion times.
3. Third, in order to develop the control and in general to gain a tool with which
it is actually possible to have a grasp on the plant, it is going to be necessary
to obtain the analytical equations in any case.
Nonetheless, testings of results were sometimes made with PLECS mostly for
validation purposes; especially in order to assess the controller performance later,
the plant’s behavior was simulated with a PLECS circuit.
15
CHAPTER 2. MODELS OF THE CIRCUIT
2.3
Basic Analytical Models: Full-Buck and Full-Boost
T
he standard methodology deployed to obtain a model for such a switched system is to take an average of the differential equations, where the weights of
the average are basically the portions of a single cycle during which these equations
hold2 .
In a first approximation, during these times a switch turned on is considered as
an ideal short, while a switch turned off is considered as an ideal open, making it
easy to determine what the differential equations for each state are. A more detailed
description of this technique is given in the next subchapters; further documentation
can be found on (Mohan et al. n.d.) and (Cuk and Middlebrooks n.d.).
The averaging procedure removes the complications involved in the switching
mechanisms, as those are basically ignored and only the average values of the signals are modeled. On one hand, this is an advantage because it delivers a simpler
model, useful to control slow changes of the signal3 . On the other hand, the information on the exact value of the signals at the switching times is lost, making it an
unusable tool for precise assessments of performance benchmarks such as switching
and conduction losses.
This is the basic reason why the feedback controller for the complete buck-boost
topology, which is based on this averaged model, only takes care of things such as
ensuring stability and disturbances rejection; since it cannot ”understand” how to
control better in terms of switching and conduction losses, this part of the controlling mechanism is lend to another structure. This is going to be discussed further in
Chapter 3 and 4.
In the next Sections, 2.3.1 and 2.3.2, the two separated models for the full-buck
and for the full-boost operation modes are first discussed. After those, a model
which fully discloses all the degrees of freedom the circuit makes available and
which describes the complete buck-boost functionality of the circuit is described.
It is to be noted that the first two models are a special case of the last one (both
in functional and mathematical sense), as it is also going to be shown.
Yet, it is also very important to observe that the complete description brings
along a number of features that are not to be found in the separated topologies; these
features are unique and show that the complete description is not just a patchwork
of the two simpler models, therefore making it a more complex reality which needs
to be considered carefully.
3 With
documentation on DCDC Power converters modeling can be found in (?),(?),(?),(?),(?)
”slow” it is meant slower than the switching frequency
16
2.3. BASIC ANALYTICAL MODELS: FULL-BUCK AND FULL-BOOST
2.3.1
Buck Model
The circuit can be considered equivalent to a synchronous buck converter if the third
switch T3 is always turned on, i.e. if d2 = 1. A preliminary study of the ”buck mode”
is useful to show the general approach which is going to be used for more complex
modes.
It turns out in fact that this version is the most attractive one as a starting point
for a study because the differential equations describing the states of the circuit (iL
and vC ) coming from the averaging method are linear by nature. This makes the
successive development of a control for this mode of the circuit straight-forward.
The procedure is the following: first, consider the case where T1 is on, and T2 is
off; applying Kirchhoff Voltage Law (KVL) and Kirchhoff Current Law (KCL) to the
circuit depicted in Figure 2.1 leads to the following equations for the states:
diL
dvC
+ vC + R C C
dt
dt
dvC
vC
dvC RC
iL = C
+
+C
dt
vin = iL RL + L
(2.1)
(2.2)
Then, consider the complementary case where T1 is off, and T2 is on; the same
equations hold basically, if vin is taken to be zero. Again, applying KCL and KVL
diL
dvC
+ vC + RC C
dt
dt
dvC
vC
dvC RC
iL = C
+
+C
dt
0 = iL RL + L
(2.3)
(2.4)
The basic idea of the averaging technique is now the following: the set of Equations
2.1-2.2 is valid during the portion of cycle time d1 , while the other set, Equations
2.3-2.4 is valid during 1 − d1 . Multiply each set of Equations with these weights, and
then sum them together; simple algebraic reorganization of that result leads to:
diL
RL
1
RC
vin
=−
+
iL + − +
vC +
d1 (2.5)
dt
L
L L (RC + Rload )
L
1
dvC
=
iL −
vC
(2.6)
dt
For the output the following equation holds in both switching positions (no actual
averaging is therefore required):
vout = vC + C
17
dvC
RC
dt
(2.7)
CHAPTER 2. MODELS OF THE CIRCUIT
Equation 2.7 can be expanded by substituting
algebraic manipulation we obtain:
vout = vC +
dvC
dt
from Equation 2.6, and after some
RC
(2.8)
Equations 2.5-2.6 and 2.8 are the averaged model. It is evident that a matrix version
of these equations in the standard linear state-space description can be obtained
without the need of an additional linearization because, as can be observed (and
as explained previously), the equations are linear in the states and in the inputs by
themselves. The standard matrix formulation is:
ẋ = Ax + Bu
(2.9)
y = Cx + Du
with:
i
x= L
vC
(2.10)
u = d1
and:
&quot;
A=
− RLL +
h
C
− L1 + L(RCR+R
1
+RC )
RC
+RC
1−
RC
#
B=
i
vin /L
0
(2.11)
D= 0
The equivalent buck topology which is achieved by setting d2 = 1 lacks of the
degrees of freedom which are given by the full (d1 , d2 , φ) coordinates when they can
be chosen independently in [0, 1]3 . This is why there is no optimization with respect
to losses for this topology: because there is only one possible d1 which is able to
reach a given vout demand.
This is also why, as it can be verified, a simple feedback approach (as opposed
to a combined feedforward and feedback approach, as discussed later) is enough to
control this scheme.
2.3.2
Boost Model
The circuit in Figure 2.1 becomes equivalent to a synchronous boost converter if the
first switch, T1, is always on, i.e. if d1 = 1.
It turns out that this version does not have linear differential equations coming
automatically out of the averaging procedure.
18
2.3. BASIC ANALYTICAL MODELS: FULL-BUCK AND FULL-BOOST
This is one of the most important difficulties encountered while developing a
controller for this mode of the circuit and for the whole buck-boost topology: the
set of differential equations governing them is a set of non-linear equations (nonlinear both in the states and in the inputs). It is therefore mandatory to make
some sort of linearization at some point (being it a single linearization or multiple linearizations arising in a PWA approach) in order to apply standard control
techniques. Control of a non-linearized plant should not be undertaken in a first
approach for the control strategy.
Thus, it is clear that more care is needed to gain the model this time compared
to the buck version, while the procedure is basically the same: first determine the
set of differential equations valid in each of the two circuit states emerging from the
two switching positions of T3 and T4 (step 1). Then multiply them by the portion of
time in which they are active, and then sum them together (step 2). This is the set
of non-linear differential equations described in the previous paragraph. This set of
equations is then linearized around some operating point, and only then a standard
matrix formulation of the state-space model can be formulated (step 3).
It is to be noted that formulating the set of non-linear equations in terms of some
matrices prior to linearization is possible to a certain extent, but can be confusing
and is therefore avoided here. Also note that the equations are first merged and
only after that they are linearized.
(step 1) The equations are again gained with a combination of KVL and KCL,
with a procedure basically equal to the previous case; after algebraic manipulations,
we achieve the equations valid while the switch T3 is on (and T4 is off)
diL
1
=
dt
L
vin − iL RL + vC + RC
dvC
=
dt
vout = vC + RC
(2.12)
(2.13)
(2.14)
On the other hand, while T3 is off (and T4 is on), we have:
diL
1
= (vin − iL RL )
dt
L
dvC
vC
=−
dt
RC
vout = vC + vC
19
(2.15)
(2.16)
(2.17)
CHAPTER 2. MODELS OF THE CIRCUIT
(step 2) Now we can merge the equations taking their weighted sum. The multiplicative weight for the first set of equations is d2 , while the weight for the second
set is 1 − d2 . Summing them afterward results in the following set of averaged nonlinear differential equations:
diL
1
=
vin − iL RL + d2 vC + d2 RC
(2.18)
dt
L
1 d2 Rload iL − vC
dvC
=
(2.19)
dt
vout = vC + RC
(2.20)
Notice from this equations the evident non-linearity, which is due to terms such as
”d2 vC ”.
(step 3) The set can now be linearized, and we choose as linearization point the
dummy point x0 = [iL,0 , vC,0 ] and u0 = [d2,0 ]. The result is the following set of
equations:
1
d2,0
RC
diL
=
−RL + d2,0
iL +
1−
vC + ...
dt
L
L
(2.21)
1
vC,0 + RC
d2
... +
L
dvC
1
1
1
=
d2,0
iL +
−
vC + ...
dt
C
C
1
... +
d2
vout = d2,0
RC
iL + 1 −
vC
(2.22)
(2.23)
which can clearly be expressed in the standard state-space matrix form, and used
once numerical values for x0 and u0 are chosen. Explicitly, this results in the following matrix description
ẋ = Ax + Bu
(2.24)
y = Cx + Du
with:
x=
iL
vC
u = d2
20
(2.25)
2.4. BUCK-BOOST MODEL
and:
 A=
1
L

d2,0
RC
−RL + d2,0 RRCC+R
1
−
L
R
+R
C 
1
1
1
d
−
2,0
C
C
 
1
L

B=
1
C
(2.26)
(2.27)
h
i
RC
+RC
+RC
h
i
i
L,0
+RC
(2.28)
(2.29)
This concludes the introductory discussion on the basic full-boost and full-buck
topologies. In the next Section, the discussion is extended to the complete buckboost functionality and the results are going to be shown.
2.4
Buck-Boost Model
aking the discussion a step further, in this section the development of the combined buck-boost topology is presented. As noted previously, when exploiting
the degrees of freedom of this circuit, a handful of new features arises which are not
to be encountered in the previous special cases. Among others, these can be cited:
T
1. Operating the switches as discussed in Section 1.2 gives rise to four feasible
combinations of switching states 2.5 compared to the previous cases where we
only had two intermitting states. This is the reason why this converter is called
a four-state converter.
2. This also directly relates to the fact that the equations for the state-times (the
time portions, during which one of the four state is active each cycle) are more
complex than before; the solution to this problem is given below, see Section
2.4.3.
3. Previously the choice of the duty cycles d1 (for the buck) and d2 (for the boost)
was constrained by the vout output demand. For instance, in the buck mode, if
the demand was vout = 10V with a given vin = 20V , then there was only one
feasible d1 which could achieve that demand (which is d1 = 0.5 in the ideal
case). This is no longer the case, as it is going to be shown, since an infinity of
combinations of (d1 , d2 , φ) exists that achieves a given vout demand.
21
CHAPTER 2. MODELS OF THE CIRCUIT
4. This additional degrees of freedom also enable us to have a better control on
the currents flowing inside the circuit while keeping the same vout . Exploiting
this property is going to allow us to optimize the switching in order to achieve
better performance in terms of losses.
5. Another important aspect concerning the modeling is that two different modeling approaches are actually going to be taken this time. The first one is the
usual averaging-technique, which will deliver the model used for the feedback
MPC. The second one is an engineered simulation of the hybrid behavior of
the switched system, described further in Section 2.4.5; the true waveform of
iL (and not only its average) will be reproduced with this model, which will
be used as a reliable tool for assessing the losses behaviour.
In the following, the presentation of the different models needed for the evaluation
of the buck-boost functionality is organized as follows: in Section 2.4.1, the Equations valid for the mean values of the plant’s variables at steady state is briefly described; in Section 2.4.2, the Equations governing the circuit behavior in each of the
four possible states are presented, and in the subsequent Section 2.4.3, the algorithm
used in order to determine the state times (the weights of the average) is exposed.
The results of these two Sections will be combined in Section 2.4.4 in order to achieve
the average model, and in Section 2.4.5 in order to obtain the hybrid model.
2.4.1
Estimation Equations
This is a set of equations useful for determining the steady-state mean values of
different signals. They do not account for the presence of the parasitic ESRs which
are otherwise included throughout the rest of the report, but are nonetheless useful.
Output values:
d1
d2
vin d1 /d2
=
vout = vin
iout
(2.30)
(2.31)
Capacitance:
vC = vout
(2.32)
iC = 0
(2.33)
22
2.4. BUCK-BOOST MODEL
Inductance:
(2.34)
vL = 0
iL =
2.4.2
vin d1 /d2
(2.35)
Equations for the four States
For each of the four states depicted in Figure 2.5 the corresponding sets of governing
differential equations is given here below. These differential equations are obtained,
as usual, by applying KVL and KCL laws and then by algebraically reorganizing
them, similar to what has been done in Sections 2.3.1 and 2.3.2. The denotations are
to be understood as follows: State ”13” means that switches T1 and T3 are currently
on; State ”23” means T2 and T3 are on, and so on.
a)
c)
C
Vin
L
RL
C
L
RL
RC
RC
b)
d)
C
Vin
L
RL
C
L
RL
RC
RC
Figure 2.5: Pictures of the circuit in its four possible states.
23
CHAPTER 2. MODELS OF THE CIRCUIT
State ”13” (see Figure 2.5.a)
diL
1
RC
RC
=
vin − iL RL +
− vC 1 −
dt
L
dvC
vC
1
= iL −
dt
C (1 + RC /Rload )
vout = vC + RC
(2.36)
(2.37)
(2.38)
State ”14” (see Figure 2.5.b)
1
diL
= [vin − RL iL ]
dt
L
dvC
vC
=−
dt
vC
vout = vC − RC
State ”23”4 (see Figure 2.5.c)
diL
1
RC
RC
=
−iL RL +
− vC 1 −
dt
L
vC
1
dvC
= iL −
dt
C (1 + RC /Rload )
vout = vC + RC
(2.39)
(2.40)
(2.41)
(2.42)
(2.43)
(2.44)
State ”24”5 (see Figure 2.5.d)
diL
−RL
=
iL
dt
L
vC
dvC
=−
dt
vC
vout = vC − RC
2.4.3
(2.45)
(2.46)
(2.47)
Equations for the State-Times
The following equations will model the time the circuit spends in each of the four
states for a given (d1 , d2 , φ) coordinate. Notice that these equations also respect the
4 Notice
5 Again,
that is basically the same set of equations as the previous one, if vin is taken to be zero.
this is the same set of equations of State 14 if vin is set to zero.
24
2.4. BUCK-BOOST MODEL
(relative) order in which these states appear6 .
Notice how a partitioning in cases was necessary to model the state-times correctly: a case-choice is to be made each time in order to fetch the valid set of equations (in figure 2.6 the correspondence between case and switching condition is depicted).
a)
d)
b)
e)
c)
f)
Figure 2.6: Diagrams of the different possible gate signals.
The symbols are understood as follows: t13 is the time spent while T1 and T3 are
on (therefore T2 and T4 are off). Similarly, t23 means T2 and T3 are on, and so on;
interpret the following as a MATLAB-code snippet (i.e. nested ”if” routines where
necessary).
Case 1 (Figure 2.6.a):
if (phase&lt;=d1) &amp;&amp; (mod(phase+d2,1)&lt;= d1)
if ((phase+d2 - mod(phase+d2,1)) == 0)
t14 = phase;
t13 = d2;
6 While the relative sequence of states is not a relevant information for the averaged model, it is in fact
fundamental for properly simulating the switching behavior which is done in Section 2.4.5 in order to
gain the hybrid model
25
CHAPTER 2. MODELS OF THE CIRCUIT
t14 = d1 - (d2+phase);
t24 = 1-d1;
end
Case 2 (Figure 2.6.b):
if ((phase+d2 - mod(phase+d2,1)) ˜= 0)
t14 = phase - (1-d2);
t13 = 1-d2;
t23 = d1-phase;
t24 = 1-d1;
end
end
Case 3 (Figure 2.6.c):
if (phase&gt;=d1) &amp;&amp; (mod(phase+d2,1)&gt;= d1)
if ((phase+d2 - mod(phase+d2,1)) == 0)
t14 = d1;
t24 = phase-d1;
t23 = d2;
t24 = 1-phase-d2;
end
Case 4 (Figure 2.6.d):
if ((phase+d2 - mod(phase+d2,1)) ˜= 0)
t13 = d1;
t14 = phase-(1-d2)-d1;
t24 = 1-d2;
t23 = 1-phase;
end
end
Case 5 (Figure 2.6.e):
if (phase&lt;=d1) &amp;&amp; (mod(phase+d2,1)&gt;= d1)
t13 = phase;
t14 = d1-phase;
t13 = phase+d2-d1;
t23 = 1-phase-d2;
end
26
2.4. BUCK-BOOST MODEL
Case 6 (Figure 2.6.f):
if (phase&gt;=d1) &amp;&amp; (mod(phase+d2,1)&lt;= d1)
t13 = phase-(1-d2);
t23 = d1-(phase-(1-d2));
t24 = phase-d1;
t23 = 1-phase;
end
Based on the results of these two first Subsections, it is now possible to estabilish
the equations for the averaged and hybrid models.
2.4.4
The Averaged Model
Multiplying the state-equations (2.38)-(2.47) with the corresponding state-times already provides the set of non-linear differential equations used to get the averaged
model. Explicitly, this results in:
diL
1
RC
RC
=
vin − iL RL +
− vC 1 −
t13 + ...
dt
L
1
... + [vin − RL iL ] t14 + ...
L
1
RC
RC
... +
−iL RL +
− vC 1 −
t23 + ...
L
−RL
iL t24
... +
L
(2.48)
vC
1
t13 + ...
C (1 + RC /Rload )
−vC
t14 + ...
... +
vC
1
... + iL −
t23 + ...
C (1 + RC /Rload )
−vC
... +
t24
dvC
=
dt
iL −
(t13 + t23 ) + ...
vout = vC + RC
vC
... + vC − RC
(t14 + t24 )
27
(2.49)
(2.50)
CHAPTER 2. MODELS OF THE CIRCUIT
Equations 2.48-2.49 can be rewritten in a more compact form as follows:
i˙L
= F (iL , vC , vin , d1 , d2 , φ) =
v˙C
vin
vin
i
i
t13 (d1 , d2 , φ) + F14 L +
t14 (d1 , d2 , φ) + ...
= F13 L +
vC
vC
L
L
iL
i
... + F23
t23 (d1 , d2 , φ) + F24 L t24 (d1 , d2 , φ)
vC
vC
(2.51)
with:

F13 = F23 =
−1
L
RL +
RC
1
−1
L
−1
1−
RC

1

(2.52)
−RL F14 = F24 =
L
0
(2.53)
Since this model is non-linear, like the boost version discussed in Section 2.3.2,
a linearization around some operating point, denoted here again x0 = (iL,0 , vC,0 )
and u0 = (d1,0 , d2,0 , φ0 ), needs to be made. Such linearization can be written in the
standard state-space matrix form yielding, for A:
A=
∂F (iL , vC , vin , d1 , d2 , φ) =
∂x(iL , vC )
x0 ,u0
= F13 t13 (d1,0 , d2,0 , φ0 ) + F14 t14 (d1,0 , d2,0 , φ0 ) + ...
(2.54)
... + F23 t23 (d1,0 , d2,0 , φ0 ) + F24 t24 (d1,0 , d2,0 , φ0 ) ∈ R2&times;2
The values t13 (d1,0 , d2,0 , φ0 ), t14 (d1,0 , d2,0 , φ0 ), ..., which are still left undetermined
in this last result have to be determined with the right case-choice from the ”if”structure presented in the previous Section.
28
2.4. BUCK-BOOST MODEL
For B, we do partial derivation with respect to the inputs, yielding:
∂F (iL , vC , vin , d1 , d2 , φ) =
B=
∂u(d1 , d2 , φ)
x0 ,u0
vin ∂t13 (d1 , d2 , φ) iL
=
F13
+ ...
+
vC
L
∂u
x0 ,u0
vin ∂t14 (d1 , d2 , φ) iL
... +
F14
+
+ ...
vC
L
∂u
x0 ,u0
∂t23 (d1 , d2 , φ) i
... + F23 L
+ ...
vC
∂u
x0 ,u0
iL ∂t24 (d1 , d2 , φ) =
... + F24
vC
∂u
x0 ,u0
#
&quot;
iL,0
0
0
∈ R2&times;3
= vin
−RC iL,0
0
L
(2.55)
Notice that the last equality in Equation (2.55) is not trivial. First, only after
computation of the partial derivative of the state-times with respect to the inputs,
i.e.:
∂t14 (d1 , d2 , φ) ∂t23 (d1 , d2 , φ) ∂t24 (d1 , d2 , φ) ∂t13 (d1 , d2 , φ) ,
,
,
∂u
∂u
∂u
∂u
x0 ,u0
x0 ,u0
x0 ,u0
x0 ,u0
it turns out that for each ”if”-case the B matrix looks always the same, while potentially six different matrices could have arisen for each of the six cases. Second, notice
how the last column of B contains only zeros; this confirms the statement made in
Section 1.4, where it was asserted that the mean of the output voltage does not depend on φ. Based on this model, it actually turns out that none of the mean values
(i.e. also the internal states’ mean values) changes if the phase is varied7 .
For the C and D matrices, the derivation of the output Equation with respect to
the states and the inputs has to be made, leading to8 :
C=
h
+ t23 )
1−
RC
i
∈ R1&times;2
(2.56)
x0 ,u0
Rload RC ∂(t13 + t23 ) ∈ R1&times;3
D = iL,0
∂u
x0 ,u0
(2.57)
7 The fact that the output mean value is not influenced is in fact a consequence of this fact, and therefore
only indirectly a consequence of the fact that the last column of B only contains zeros.
8 In the MATLAB routines attached to this document, approximations for these matrices have usually
been made. In particular, C has been approximated to be C ≈ [0 1] and D ≈ [0 0 0]
29
CHAPTER 2. MODELS OF THE CIRCUIT
This ends the discussion about the models needed for the feedback part of the
control scheme, discussed in Section 4.4.2.
2.4.5
The Hybrid Model
Hybrid model might sound exotic, but it is developed here in the (probably) most
intuitive way without resorting to tools such as HYSDEL9 . This is done by using the
information on the state times and their sequence from the previous Sections 2.4.22.4.3: basically, the sequence in which the states appears is known, and their active
time is known as well. The voltage across the inductor is easily estimated with the
following equations:
State ”13”:
vout
vL = vin − vC + iL −
− iL RL
(2.58)
vout
− iL RL
vL = − vC + i L −
(2.59)
vL = vin − iL RL
(2.60)
vL = −iL RL
(2.61)
vL
dt
L
(2.62)
State ”23”:
State ”14”:
State ”24”:
Then, integrating
diL =
over the period of time during which each state is active (thereby approximating
with the assumption that vL be constant) readily gives the desired waveform with
the small addition that the offset still needs to be adjusted. This can be done either
by using the approximative equation for the mean iL at steady state (2.35), which is
done here (because of its simplicity) with good results, or by enforcing the constraint
that the current at the beginning of each period must be equal the current at the end
of it.
9 http://control.ee.ethz.ch/˜
hybrid/hysdel/
30
2.4. BUCK-BOOST MODEL
Figure 2.2 shows for one given (d1 , d2 , φ) coordinate that the two approaches
agree up to 0.1%; it can be verified that the prediction for iL obtained this way is
accurate also for other choices of u.
As stated previously, the results from this hybrid model are used afterward to
make assessment regarding the behavior of switching and conduction losses; this is
going to be the main topic of Chapter 3.
31
Chapter 3
Power Losses
Ploetzliche Regenfaelle koennen zum Betreten einer
Buchhandlung zwingen.
Loriot
3.1
Introduction
ur objective is to drive the circuit while minimizing the losses occurring inside
of it. In order to do this, models for the behavior of these losses are necessary;
i.e. the aim of this Chapter is to present how this mapping, Ploss = f (d1 , d2 , φ), can
obtained.
O
3.2
Losses Description
T
here are two different types of losses occurring inside the circuit: Conduction
Losses (PConduction ) and Switching Losses (PSwitching ); in the following, these
two types of losses are going to be shortly described.
Conduction Losses
These are losses of resistive type, and, for the particular circuit that is investigated, they are produced because of current flowing through the following resistive
media:
• MOSFETs’ channel resistance RDS,on
• MOSFETs’ body diode
• Capacitance’s ESR (Equivalent Series Resistance)
• Inductance’s ESR
33
CHAPTER 3. POWER LOSSES
Being these losses of resistive type, they can be modeled to be proportional to the
square of the RMS value of the current flowing inside the circuit, i.e.:
PConduction ∼ i2L,rms
(3.1)
Note: this estimation is already an approximation, since higher losses will usually cause a rise of temperature, which is going to affect the values of the parasitive
resistances, which in turn is going to affect the losses.
Switching Losses
The mechanisms involved in the production of switching losses are more complicated than the previous ones. They are produced by the action of turning on
and off active devices on the power’s path, therefore they only happen at discrete
times ”tj ” (where j indexes all the times at which switchings of a given MOSFET
occur) and for a short period; they occur under the following circumstances (Mohan
et al. n.d.):
• switching of power currents (”turning on and off currents in the presence of
voltage”)
• parasitic drain capacitance charge and discharge
• gate drive losses
• body diode reverse recovery
In the part of investigation conducted on switching losses, only the first contribution
(i.e. switching losses due to switching of power currents) has been considered. An
estimate of their magnitude can be obtained as follows:
• If the MOSFET is turning on, and the current was not flowing through its
body diode, then the switching loss can be estimated as being proportional to
the product of the current that will start to flow through the MOSFET and the
voltage across the MOSFET prior to switching, i.e.:
+
PSwitching,j ∼ v(t−
j )iL (tj )
(3.2)
• If the MOSFET is turning off, on the contrary, and the current will not be able
to flow through its body diode, then the switching loss can be estimated as
being proportional to the product of the current that was flowing through it
and the voltage that will be applied to the MOSFET after, i.e.:
−
PSwitching,j ∼ v(t+
j )iL (tj )
34
(3.3)
3.3. MODELING POWER LOSSES
because of this, if the current iL is positive (flowing from the input stage to the output stage), then switching losses will occur only at switches T1 and T4. Conversely,
if iL is negative, then switching losses will occur in switches T2 and T3.
On a side note, it can be noted that since these losses occur at switching times,
the more switchings there are, the higher the switching losses will be (if the same
MOSFETs are used), i.e. switching losses grow proportionally to the switching frequency. Therefore, on one hand, switching frequency should not be chosen to be
arbitrarily high. But on the other hand, switching frequency should not be chosen
too low either because that would cause higher ripples on the output voltage.
Also, it is of critical importance to note at this point that during the simulations
described further in this chapter, the magnitude of the losses is estimated using these
very equations. But since these equations only give results that are proportional to the
exact values, their shape will describe the general behavior of the losses properly,
but their magnitude will need to be corrected by an adequate multiplicative correction constant. This constant will strongly depend on the choice of components that
is going to be made. This aspect is discussed more in detail in the next Section.
3.3
Modeling Power Losses
tis possible to achieve a characterization of the switching and conduction losses
using the equations of above, by estimating their value for each choice of (d1 , d2 , φ).
This is accomplished by using the model (2.58)-(2.61) discussed in Section 2.4.5,
which reproduces the hybrid behavior of the plant; explicitly, for each combination
of (d1 , d2 , φ), the shape of iL is calculated. This enables the calculation of the conduction losses. Then, the equations for the state-times exposed in Section 2.4.3 are
used to fetch the sequence of switchings occurring during each cycle, and the times
at which each switching occurs. This information is sufficient in order to calculate
the switching losses.
Notice that such a detailed description (in contrast to a simplified version given
by the averaged models) is needed in order to assess losses, first because the RMS
value of the current is needed (which can only be calculated if the correct shape of
the iL current is known), and second because the losses also depend on the sequence
of switchings which is adopted, which therefore needs to be known (what switch is
switched when, so that the current can be multiplied with the correct voltage).
The losses have been investigated for many vout /vin ratios, i.e. the set of (d1 , d2 , φ)
combinations giving the same output voltage has been gridded and then simulated
for each of the resulting knots. Typical results for low output references and for high
output references are shown in Figure 3.3 and 3.3.
I
35
CHAPTER 3. POWER LOSSES
Figure 3.1: Switching and Conduction Losses for a low vout /vin demand. Values are not
scaled properly, but do give an idea of the percentages. Notice that the switching losses have
a pretty marked behavior in this case; this is probably due to resonant occurrences inside the
circuit which should be investigated further. This image has been selected mainly because it
shows well how conduction and switching loss minima-locations do not necessarily coincide.
A fundamental result of this study is shown in Figure 3.3: it is evident from the
two pictures that the positions at which conduction losses and switching losses
are minimal do not necessarily coincide. The same result can be verified in Figure
36
3.3. MODELING POWER LOSSES
Figure 3.2: Switching and Conduction Losses for a high vout /vin demand. Values are not
scaled properly, but do give an idea of the percentages. The switching losses behavior depicted for this case is more representative of what is usually encountered.
3.3. This poses a challenge in the following sense: if the two minimal positions
would coincide, then the minimum of the sum of the two losses would also be at
that same place; but since they do not, it is important to sum up together the correct
values for conduction and switching losses, in order to achieve the correct position
of the minimum of the total. On one side, this means that sometimes there needs to
37
CHAPTER 3. POWER LOSSES
be an optimized trade-off between these two types of losses. On the other hand, this
also means that the exact values of the two losses will influence the position of the
minimum of the sum. Or stated another way round: we saw that the exact values
of switching and conduction losses still depend on the value of the multiplicative
correction terms that need to be applied; these values in turn depend on the choice
of components, and as a consequence the value and position of the minimum total
loss will depend on the choice of components.
Conduction Losses vs
Swiching Losses
phase = 0.8
phase = 0.2
1.17
1.16
i L (A)
1.15
1.14
1.13
1.12
1.11
1.1
2.96
2.964
2.968
2.972
time (ms)
2.976
2.98
2.964
2.968
2.972
time (ms)
2.976
2.98
Switching Loss (~)
30
25
20
15
10
5
0
2.96
Figure 3.3: Comparison between conduction (upper graph) and switching losses (lower
graph) for two different (d1 , d2 , φ) combinations giving both vout /vin = 0.2 (both have
d1 = 0.14, d2 = 0.7). On the first graph, it is clear that the blue curve, with φ = 0.2 has
overall lower conuction losses, because its RMS value is lower than the red one. On the lower
graph, however, where switching losses are depicted (as losses occurring only at discrete
times) it is clear that the blue inputs combination delivers higher switching losses. Therefore,
it is verified that conduction and switching losses do not necessarily have coinciding minimal
points, since lowering one might cause an increase in the other.
For the successive development of the controller, which will depend on these
38
3.3. MODELING POWER LOSSES
results, a certain choice for the corrective weights has been made, which is in no
way bound to any actual choice of components - as it should. This choice has been
made for prototyping purposes, and appropriate weight still need to be determined
from a proper selection of components.
39
Chapter 4
Controller Design
Short cuts make long delays.
Hobbit Proverb
4.1
Introduction
ased on the research done on the models in Chapter 2 and the Losses in Chapter
3, it is now possible to start developing an efficient control for the plant. As a
reminder, our task is to control the duty cycles of each pair of transistors and their
phase, so as to ensure:
B
• first and most important: reaching of and stabilizing around a given output
voltage demand;
• reaching the target steady state should happen in the desired manner, i.e. the
controller needs to handle transients properly;
• the controller also needs to be able to reject disturbances (usually encountered
on the load and on the input voltage source vin );
• while doing all this, the controller (in the full buck-boost mode) needs to
choose among the infinite possibilities of inputs, that would satisfy the above
conditions, those that will cause the least losses.
Notice that, as explained previously, minimization of losses is only possible if all
the degrees of freedom of the circuit are exploited, i.e. there is no optimization of
control strategy towards minimal losses for the buck or boost modes alone.
The presentation of the development of such a controller is going to be handled
in the next Sections as follows: first the choice of MPC as control approach for the
feedback is motivated; in Section 4.3 the control techniques deployed for the basic
versions of the circuit (full-buck and full-boost) are discussed, based on which the
more complex control for the whole buck-boost functionality is finally going to be
discussed, Section 4.4.
41
CHAPTER 4. CONTROLLER DESIGN
4.2
Why MPC
MPC was chosen based on the following reasons:
• its ability to efficiently deal with constraints
• because standard control techniques such as a PID implementation delivered
unsatisfactory results (Frehner 2007)
• because of its robustness to model uncertainties
• and because it works for multiple inputs plants exactly the same way as it
works for the SISO ones, thereby allowing analogous implementations for the
buck/boost (one input) and buck+boost (three inputs) modes; on the contrary,
traditional implementations using control techniques such as PIDs present
many challenges when applied to multiple inputs plants, especially when it
comes to the assessment of control performances such as stability, robustness
and disturbances rejection.
Also, while MPC was previously only applied to slow-dynamics plants, because the control action needed to be calculated from an optimization problem each
time (on-line control), recent developments made it possible to move the burden of
calculating the control moves off-line, through what is called a multi-parametric
(quadratic in this case) program approach (mpQP), thus allowing to apply MPCs
also to plants with fast dynamics (IfA Website1 ).
Calculating the input move on-line would consist in solving a quadratic program
(QP) each time (if 2-norm cost functions are taken - as it is usually the case), and a
multiparametric approach just means that this optimization problem is parametrized
and solved for a certain set of parameters x (this is the set of all the x’s contained
in a predefined constrained set X), and during runtime the correct input is easily
computed by plugging in the parametrized solution the appropriate current state x.
Multiparameter problems allow to calculate so-called explicit MPCs. An explitic
MPC is therefore nothing more than a look-up table that will return the control move
to be taken given some current state x. Notice that this is the same control move that
would be applied if an on-line MPC strategy would be chosen instead of the explicit
one.
Since the control moves are pre-calculated moving around the whole feasible
U &times; X set, it is necessary to constrain it in order to make this procedure end in finite
time. This is why this technique can be applied only to constrained problems. For
this particular plant, the inputs, which are basically the duty cycles and the phase,
1 see
(IfA n.d.) http://control.ee.ethz.ch/ hybrid/control.php
42
4.3. BASIC CONTROL STRATEGIES
need to be bounded between 0 and 1. This results in the inputs set U to be delimited
to:
(4.1)
U = u ∈ R3 | u ∈ [0, 1]3
X, on the other hand, is only ”loosely” constrained, i.e. it is taken to be much larger
then the actual values of the states are ever going to be driven at, just to make the
computation feasible. Notice that other restrictions could be made, in particular a
restriction on the inductor current to be non-negative (if discontinuous mode has
to be considered) and other restrictions to ensure soft-start requirements could be
4.3
4.3.1
Basic Control Strategies
Buck, the Simplest Mode
s discussed in Chapter 2.3.1, the model obtained with the averaging technique
is linear. There is only one variable being controlled (d1 ) and there is no optimization of controls towards least losses. This is why a simple feedback approach
(as opposed to a combined feedforward and feedback approach, as discussed later)
is enough to control this scheme.
The feedback control is implemented as an explicit MPC. The MPC is to be fed
with a discrete time model which is easily obtained from the continuous model of
Equations (2.24)-(2.29) with the c2d() routine in MATLAB . Thus, starting from these
equations and plugging in testing values for the components reported in Table 4.1,
the following discrete-time model results:
A
xk+1 = Axk + Buk
(4.2)
yk = Cxk + Duk
(4.3)
with:
0.9992 −0.0498
A=
0.03321 0.9925
0.9997
B=
0.01662
C = 0.9998 0.0009998
D = ;
2 http://control.ee.ethz.ch/˜
hybrid/applications.php#powerElectronics
43
CHAPTER 4. CONTROLLER DESIGN
Full-Buck, Full Boost
Buck-Boost
Sampling Frequency
vin
L
C
RL
RC
vin
L
C
RL
RC
TS
20V
2e−5 H
3e−5 F
5Ω
0.001Ω
0.001Ω
20V
2e−4 H
3e−5 F
5Ω
0.001Ω
0.001Ω
100kHz
Table 4.1: Values of the components used to test the behavior of the controller.
A typical start-up response of the controlled output can be seen in Figure 4.1.
The details of this implementation, such as the influence of the weights and of the
prediction horizon, are not discussed further, since this is a special case of the complete buck-boost topology; such discussions are going to be made for the general
case in Section 2.4.
4.3.2
Boost Mode and the Gain Scheduling Technique
nalogous to the Buck operation mode, only one variable is being controlled
(d2 ) and no optimization towards least losses can be done. Therefore also in
this case a simple feedback controlling strategy (i.e. not a combined feedforward +
feedback) is taken. And again, the feedback is based on a MPC approach, and the
discrete-time model is obtained with c2d() like before.
But the boost operation mode presents a challenge which was not encountered
for the buck mode: since the model obtained with the averaging technique is not
linear, the matrices obtained linearizing around x0 and u0 still depend on the numerical values of x0 and u0 , as can be seen from Equations (2.24)-(2.29). This could
indeed be a major problem because basically it means that different steady-state operating points have different dynamics. Notice that this challenge is to be faced in
the full buck-boost operation mode as well.
Fortunately, it turns out that while the plant driven in boost mode is indeed nonlinear, it still behaves ”well” (see below for what is meant with ”well”).
This fact has been exploited both for the boost and the buck-boost mode. The
reasoning behind it is the following: the fact that the model is non-linear is a reality
which cannot be avoided and needs to be accounted for; the most basic approach to
A
44
4.3. BASIC CONTROL STRATEGIES
Full-Buck Output Performance
7
6
vout (V)
5
4
3
2
1
0
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
time (ms)
Figure 4.1: Startup performance of the controller developed for the full-buck operation, with
vout,ref = 6V . Small ripples are visible at steady state, since noise was added to vin in order
to make a first assessment of noise rejection performance.
face this problem from a control perspective is to apply a Gain Scheduling Technique3 : this technique basically consists in linearizing the system around as many
steady-state points as possible (and at least so many as to ensure the basic requirements of controlling) and then to design a control for each of these linearizations; afterwards switching policies between these multiple controls need to be established,
thereby obtaining a control that works for the whole region.
In the boost case, due to its low degree of non-linearity, only one of such linearizations is strictly necessary in order to ensure reaching of the target steady-state
vout and stabilization around that point. Of course, the best performance is obtained
when the target steady-state point is at or near to the point around which the model
of the plant was linearized. Taking output demands far from that point result in a
degradation of performance during transients. The extent of this performance loss
depends on the choice of components, but this has not been investigated further;
yet, later in Section 2.4 the discussion on the whole buck-boost functionality is going to lead to this same topic again, and it is going to be shown that indeed the plant
behaves ”well”.
3 http://control.ee.ethz.ch/˜
apnoco/Lectures/lec08.pdf
45
CHAPTER 4. CONTROLLER DESIGN
Pluggin numerical values for the components in the continuous model (2.24)(2.29), choosing as a linearization point vout /vin = 1.5 and then obtaining the discretetime model from c2d() results in the following matrix description:
xk+1 = Axk + Buk
(4.4)
yk = Cxk + Duk
(4.5)
with:
−0.01993
0.9932
−2.509
B=
0.8137
C = 0.0003999 0.9998
A=
0.9998
0.01329
D = [0.025];
The control scheme deployed for this mode can be seen in Figure 4.2. A typical
start-up response of the controlled output can be seen in Figure 4.3. Also in this
case the details of the implementation, such as the influence of the weights and
of the prediction horizon, are not discussed further since this is a special case of
the complete buck-boost topology; such discussions are going to be made for the
general case in the next Section.
4.4
Buck-Boost Operation
T
he buck-boost implementation is similar to the boost one in that non-linearity is
still present. Other than that, it turns out that exploiting the possibilities given
by the full buck-boost operation requires additional care because:
• there are now multiple inputs
• control actions also need to drive the plant while ensuring least possible losses
While going from a single input to a multiple inputs plant is done smoothly
with an MPC approach, losses control require additional care. In this work, the way
the handling of losses has been achieved is by applying a combined feedforward
and feedback control structure (rather than a feedback control alone, as done previously). A simplified abstraction of the control scheme that enlightens the control
flow can be seen in the block diagram in Figure 4.4. The more detailed Simulink
implementation, including all the subsystems used, can be seen in Figure 4.4.
46
4.4. BUCK-BOOST OPERATION
Figure 4.2: Control scheme deployed for the full-boost operation mode. The feedforward element is nothing more than an implementation of the Equations 2.4.1 used to achieve approximatively the right output voltage. The finer control towards the exact value is undertaken
by the feedback MPC. The addition of noise on vin is also visible. The PLECS circuit was
substituted with an implementation of the Equatins in 2.3.2.
4.4.1
The Feedforward
The feedforward control (denoted as precalculated look-up table in the block diagram) is responsible for the major contribution to the signal u. It is by far the simplest approach with which complications such as proper treatment of losses can be
handled.
The feedforward table is constructed as follows: recall that in Chapter 3 the investigations led to two surfaces for each vout /vin demand telling us how switching
and conduction losses behave with respect to the inputs 4 . The information from
these surfaces has been used here as follows: the total loss for each given point is a
weighted sum of switching and conduction losses. The weights can be adjusted and
can potentially lead to different feedforward tables, but the essence of the procedure
is the same. In this work, the weighting has been the following5 :
Ploss,T otal = 0.7Pswitching + 0.3Pconduction
(4.6)
These total losses are then compared between each other for each vout /vin demand
and the combination of inputs that leads to the least total loss is chosen and inserted
4 Notice
that all the losses mentioned in the current discussion are losses calculated at steady state.
the weights could be different based on the true value of the components chosen (and they
do not necessarily need to add up to 1).
5 As stated,
47
CHAPTER 4. CONTROLLER DESIGN
Full-Boost Output Performance
40
35
30
vout (V)
25
20
15
10
5
0
0
0.5
1
1.5
2
2.5
time (ms)
3
3.5
4
4.5
5
Figure 4.3: Startup performance of the controller developed for the full-boost operation, with
vout,ref = 25V . Again, small ripples are visible at steady state, since noise was added to
vin in order to make a first assessment of noise rejection performance. Further, the initial
overshoot can be regulated with a proper choice of the weights, which is not shown but that
can be verified.
v in
vout,ref
Precalculated
(look-up table)
u0
x0
MPC-Based Feedback
+
u
&ucirc;
PWM
Circuit Model
(PLECS)
vout
x
Figure 4.4: Block diagram of the control scheme deployed for the full buck-boost operation
mode.
in the feedforward lookup table.
It is to be noted again that, under these circumstances, changing the weights can
and will change the optimal u to be plugged into the feedforward table.
Feedforward control therefore ensures that among all the infinite possibilities of
48
4.4. BUCK-BOOST OPERATION
Global Scheme
Feedback and Feedforward Blocks
PWM Block
Figure 4.5: Detailed scheme of the control scheme deployed for the whole buck-boost functionality.
49
CHAPTER 4. CONTROLLER DESIGN
(d1 , d2 , φ) control combinations that will steer the plant to the desired output demand at steady state, only the ones around that one which will cause the least losses
are going to be selected.
In general, feedforward control ensures that the plant will be steered to (at least
approximately) the right state even if the feedback were to be switched off. It is
straightforward in that no mathematical model needs to be elaborated and then
(somehow) patched together with the MPC, which would considerably increase its
complexity and size (in memory).
The typical content of a feedfoward table is depicted in Figure 4.4; notice how,
for the specific choice of weights of above, the control usually tries to drive the plant
with the highest d2 possible. This is directly related with the fact that the current in
the inductor iL (which increases with decreasing d2 , see Equations in Section 2.4.1)
is a direct measure for the conduction losses.
Figure 4.6: Figure depicting the typical content of a feedforward table. Each point corresponds to a certain vout /vin ratio.
50
4.4. BUCK-BOOST OPERATION
4.4.2
The Feedback
The feedback part is going to be similar to the previous implementations (Section
4.3.2), i.e. it is the part of the controlling structure that will ensure reaching of the
desired steady-state output and rejection of disturbances.
The difference from the previous cases is that this time the MPC will steer the
plant to x = 0 ∈ Rn (therefore y = 0 ∈ R). So instead of handling controls towards
absolute values for the output, the feedback will correct signals deviating from the
desired steady state, i.e. the input to the MPC is not going to be x anymore (remember, MPC is a state-feedback control) but rather x − x0 , a strategy usually referred
to as ”control towards origin” for obvious reasons. For this reason, one can expect
that the control actions from the MPC are going to be zero once the steady state has
been achieved, if the feedforward control was precise enough and if there are no
disturbances.
Since the plant is non-linear, the gain scheduling technique is going to be used
also here. This time, however, two different linearizations are going to be made
instead of one. This is due to the plant working in two regions where different set
of equations for the state times hold (see Section 2.4.3). Explicitly,
• For the range vout /vin ∈ [0.05, 0.4], the set of equations for the state-times that
is valid is the 5th
• For the range vout /vin ∈ [0.4, 4], the set of valid equatins is the 2nd one instead.
The simplest approach to deal with this aspect is therefore to linearize once around
some point contained in the first range, and then linearize once more around another point in the second range. For this work, the two linearization points chosen
are vout /vin = 0.2 for the ”lower” linearization, and vout /vin = 1.5 for the ”upper”
one. The values of u0 and x0 deriving from these choices, which are what is actually needed, are going to be fetched from the feedforward control component (see
below).
The policy adopted to switch between these two MPCs is straightforward: if
vout /vin is smaller than 0.4 then choose the first one, if vout /vin is larger than 4, then
choose the second one. Two things need to be noted here:
• output demands out of the actual ranges where the equations are valid (i.e.
vout /vin smaller than 0.05 or larger than 4) are still treated with the corresponding one of these two MPCs, because they shouldn’t be requested to start with,
and even if they are, the output performance is still decent (see Figure 4.12) in
part due to the feedforward action.
• disturbances in vin should also be considered. If the input voltage vin is measured, then the correct vout /vin ratio is calculated and the correct lineariztion
51
CHAPTER 4. CONTROLLER DESIGN
is chosen, therefore no problem arises. If instead vin cannot be measured, and
disturbances to it would actually cause a switch of used controller, but this
switches are not accomplished because these disturbances are not measured,
then the need to rely on the robustness of the control arises. This is an aspect that should be investigated further (along with a precise assessment of
the controller’s robustness and stability).
The controller has been designed with the help of the mpt studio routine included in the mpt toolboox for MATLAB (R)6 . Other than choosing a ”control towards origin” policy for the reasons explained above, the feedback MPC has been
designed as follows: to calculate the cost function, the 2-Norm has been chosen;
the weights on the states and on the output and the prediction horizon have been
determined iteratively; the procedure has been the following: first, look at the influence to the output curve given by the output weight; since the output voltage is
basically the same signal as vC , this weight is also going to be the weight to the vC
state (therefore, a weight of zero is going to be applied to the state vC , for otherwise
deviations of vC from the desired reference would be accounted twice for); for the
”lower MPC” implementation a weight on the output of 10 has been chosen, see
Figure 4.4.2.
The prediction horizon has also iteratively been chosen to be 5, and its influence
can be seen in Figure 4.4.2. In general, it is desirable to have the highest prediction
horizon possible. High prediction horizons improve stabilization characteristics of
MPCs and in general provide better output performances; yet they cannot be chosen
arbitrarily high, since computation of the control self increases dramatically with
increasing prediction horizon, and its size (in bytes) increases considerably as well,
thus potentially making impossible their implementation on platforms with limited
storage capacities such as DSPs. After this step, the influence of the weight on the iL
state has been considered, and as can be seen in Figure 4.4.2, even while varying this
parameter by many orders of magnitude, there seems to be no appreciable influence
to the output voltage signal once the other two weights are established.
The same procedure has been applied also for the ”upper” MPC, see Figure 4.4.2.
4.4.3
Results
It can be argued that if this precalculated lookup table does indeed contain the best
values the plant (circuit) can be driven at steady state, then the contribution from the
MPC feedback can be avoided. This is of course not the case: first, looking at Figure
4.9, it is clear that the contribution from the MPC boosts the performance during
6 http://control.ee.ethz.ch/˜
mpt/
52
4.4. BUCK-BOOST OPERATION
the initial transient. Furthermore, a feedback action is always desired in any control
scheme, in order to ensure the ability to reject disturbances and model uncertainties.
The two contributions to the u signal coming from the feedback and from the
feedfoward part can be seen in Figure 4.10. As it can be seen, the MPC supplies the
plant with a contribution different than zero only during the transient. As soon as
the transient has settled, it contribution goes to zero and stays there; this is always
the case as long as no disturbances or other external influences affect the circuit; if
disturbances are indeed applied, then the MPC control is going to counter those and
its contribution is going to be different than zero.
A typical disturbance rejection done by the controller can be seen in Figure 4.11:
the blue bottom curve depicts the perturbation (in percentage) affecting the input
voltage vin , while in the upper graph, the red curve shows how this perturbation
affects the output voltage if no feedback action is taken, and the green one shows
the output if rejections are countered by the MPC.
The resulting output start-up performance for a set of different output references
can be seen in Figure 4.12; notice that the controller is indeed able to drive the circuit
both in its ”buck” mode and ”boost” mode, as specified in the objectives for this
project. Further, notice that the control is indeed able to properly drive the plant also
towards steady states different than those around which the models were linearized,
thus showing its ”well” behavior.
53
CHAPTER 4. CONTROLLER DESIGN
Output Weight Dependence
vout (V)
15
10
5
0.1
1
100
0
-5
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
3
iL(A)
2.5
2
1.5
0.1
1
100
1
0.5
0
0
0.2
0.6
time (ms)
0.8
1
1.2
i State Weight Dependence
L
15
vout (V)
0.4
10
5
0
-5
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
0.2
0.4
0.6
time (ms)
0.8
1
1.2
3
i L(A)
2.5
2
1.5
1
0.5
0
0
Prediction Horizon Dependence
vout (V)
15
10
5
1
2
5
10
0
-5
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
3.5
3
i L(A)
2.5
2
1.5
1
2
5
10
1
0.5
0
0
0.2
0.4
0.6
time(ms)
0.8
1
1.2
Figure 4.7: These Figures show the change in output response and in the iL behavior with
respect to changes to weightings for the ”lower” part of the controller.
54
4.4. BUCK-BOOST OPERATION
Output Weight Dependency
40
vout (V)
30
20
10
0.01
0.1
1
100
0
-10
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
12
10
iL(A)
8
6
0.01
0.1
1
100
4
2
0
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
iL Weight Dependency
40
vout (V)
30
20
10
0.01
0.1
1
100
0
-10
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
12
10
iL(A)
8
6
0.01
0.1
1
100
4
2
0
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
Prediction Horizon Dependence
40
vout (V)
30
20
10
1
2
5
10
0
-10
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
iL(A)
15
10
1
2
5
10
5
0
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
Figure 4.8: These Figures show the change in output response and in the iL behaviour with
respect to changes to weightings for the ”upper” part of the controller. As before, there is no
noticeable influence from the iL weight.
55
CHAPTER 4. CONTROLLER DESIGN
Feedforward vs
Feedforward + Feedback
40
feedforward
feedforward + feedback
35
30
vout (V)
25
20
15
10
5
0
-5
0
0.5
1
1.5
2
time (ms)
2.5
3
3.5
Figure 4.9: Difference of output behaviour if the MPC is deactivated.
Typical Control Action
1
0.8
d1
0.6
0
d2
0
phase
0
0.4
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
0.4
0.2
0
-0.2
-0.4
d1*
d2*
phase*
-0.6
-0.8
0
0.2
0.4
0.6
time (ms)
0.8
1
1.2
Figure 4.10: Typical control actions of the two parts of the controller. The upper graph shows
the control actions supplied by the precalculated look-up table (which are therefore constant
signals) and the lower graph shows the signals that are superimposed generated by the MPC.
56
4.4. BUCK-BOOST OPERATION
v
8.33
in
Perturbations Rejection
without Feedback
with Feedback
vout deviation %
6.67
5
3.33
1.67
0
-1.67
-3.33
-5
1.5
2
2.5
3
3.5
time (ms)
4
4.5
5
2
2.5
3
3.5
time (ms)
4
4.5
5
3
vin perturbation %
2
1
0
-1
-2
-3
-4
1.5
Figure 4.11: Disturbances rejection behavior, with and without the MPC action.
57
CHAPTER 4. CONTROLLER DESIGN
Start-Up Performance
40
2V
5V
10V
35
30
15V
20V
25V
30V
vout (V)
40V
25
20
15
10
5
0
0
0.2
0.4
0.6
time (ms)
0.8
1
Figure 4.12: Start-up performance for different output demands.
58
1.2
Chapter 5
Conclusion
The present work is a study on the circuit depicted in Figure 1.2 which is used to
achieve DC-DC power conversion.
In the first part of the work (Chapters 1-3), different models for its behaviour
have been developed, including a state-space averaged model and an hybrid one.
Based on these models simulations have been conducted in order to assess the losses
occurring inside of it. These simulations reveal that it is in general not possible to
drive the circuit while minimizing simultaneously both conduction and switching
losses. Rather, in order to drive the circuit in the most efficent way, an optimized
balance between these two losses needs to be made. Further, this balance depends
on the specific choice of components used.
In the second part of the work (Chapter 4), for a specific choice of components,
the implementation of a controller for this circuit is discussed. The controller has
been designed as working on the combined action of a precalculated look-up table
(feedforward action) and a Model Predictive Control (MPC) based feedback action.
The abilty to drive the circuit both in its boost as well its buck modes and its noise
rejection capabilty are the performance benchmarks for this controller which have
been studied.
Recommended extensions to this work include the refinement of the models to
account for parasitics and non-ideal behaviours, so as to enable a subsequent controller implementation based solely on MPC, and a more accurate evaluation of the
controller’s stabilization capabilties.
59
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