International Journal of Grid Distribution Computing
Vol. 8, No. 3, (2015), pp.97-110
http://dx.doi.org/10.14257/ijgdc.2015.8.3.10
Maximum Power Point Tracking Using an Adaptive
Perturbation and Observation Algorithm for a Grid-Connected
Solar Photovoltaic System
Duy C. Huynh1 and Matthew W. Dunnigan2
1
Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam
E-mail: huynhchauduy@ieee.org
2
Heriot-Watt University, Edinburgh, United Kingdom
E-mail: m.w.dunnigan@hw.ac.uk
Abstract
This paper proposes an adaptive perturbation and observation (P&O) algorithm for
maximum power point tracking (MPPT) control strategy of a grid-connected solar
photovoltaic (PV) system under varying atmospheric conditions. This strategy is
necessary in order to extract maximum power output from a solar PV panel. The
adaptive P&O algorithm is proposed to utilize a variable perturbation step size which
depends on power changes. The obtained simulation results are compared with those
using the conventional P&O algorithm, which show the effectiveness of the proposed
MPPT algorithm in the MPPT of a grid-connected solar PV system.
Keywords: Maximum power point tracking, perturbation and observation algorithm
and grid connected solar photovoltaic system
1. Introduction
Electricity demand in the world is increasing rapidly. Renewable energy has been
considered in recent years because it is almost free and clean. Amongst renewable
energy sources such as solar energy, wind energy, ocean energy, etc, solar energy is a
very promising energy source for electric power generation. Electricity can be
generated from sunlight either using the photovoltaic (PV) effect [1], or energy from
the sun to heat up a fluid for generating electricity. These two technologies are widely
used to supply power to either standalone loads or power systems. However, it can be
realized that the conversion efficiency of the solar PV cells is very low from 9% to
17%, especially under low solar irradiation conditions. Additionally, the electric power
which is generated by solar PV panels always changes under various weather
conditions. It is obvious that the V-I and V-P characteristics of the solar PV cell are
non-linear and vary with irradiation and temperature [2]. However, there is always a
unique point on the V-I or V-P curve called the maximum power point (MPP). This
point is not known on these characteristics, but it can be located by MPPT algorithms
categorized generally as follows: Perturbation and Observation (P&O) algorithms [3][5], Incremental Conductance (InC) algorithm [6-8], Constant Current (CC) or Voltage
(CV) algorithm [9]-[10] and other algorithms such as Fuzzy Logic (FL) algorithm [11][12], Artificial Neural Network (ANN) algorithm [13] and the Particle Swarm
Optimization (PSO) algorithm [14-15]. These existing algorithms have several
advantages and disadvantages concerned with simplicity, convergence speed, extra
hardware and cost. This paper proposes an adaptive P&O algorithm for MPPT of a
grid-connected solar PV system. The achieved simulation results confirm the
effectiveness and benefit of the proposed algorithm as compared to the results using the
P&O algorithm. The remainder of this paper is organized as follows. The mathematical
ISSN: 2005-4262 IJGDC
Copyright ⓒ 2015 SERSC
International Journal of Grid Distribution Computing
Vol. 8, No. 3, (2015)
model of solar PV panels is described in Section 2. A grid-connected solar PV system
is presented in Section 3. An adaptive P&O algorithm for MPPT strategy is proposed in
Section 4. The simulation results then follow to confirm the validity of the proposed
algorithm in Section 5. Finally, the advantages of the new proposal are summarized
through comparison with the related existing approach, P&O algorithm.
2. Photovoltaic Panel
A solar PV cell is described as follows:

I  I sc  I 0  e


V oc 

 1


(1)
 I sc

ln 
 1


q
 I0

(2)
qV
kT
kT
P  V  I  VI
sc

 VI 0  e


qV
kT

 1


(3)
where
I: the current of the solar PV cell (A)
V: the voltage of the solar PV cell (V)
P: the power of the solar PV cell (W)
Isc: the short-circuit current of the solar PV cell (A)
Voc: the open-circuit voltage of the solar PV cell (V)
I0: the reverse saturation current (A)
q: the electron charge (C), q = 1.602  10-19 (C)
k: Boltzmann’s constant, k = 1.381  10-23 (J/K)
T: the panel temperature (K)
The solar PV panels are very sensitive to shading. Therefore, a more accurate
equivalent circuit for a solar PV cell is presented to consider the impact of shading as
well as account for the losses due to the module’s internal series resistance, contacts
and interconnections between cells and modules. Then, the V-I characteristic of a solar
PV cell is written as follows:

I  I sc  I 0  e


q  V  IR s
kT

  V  IR
 1  
 
Rp
 
s




(4)
where
Rs and Rp: the resistances used to consider the impact of shading and losses.
Although, the manufacturers try to minimize the effect of both resistances to
improve their products, the ideal scenario is not possible.
Two important points of the V-I characteristic that must be pointed out are the opencircuit voltage, Voc and the short-circuit current, Isc. The power generated is zero at both
points. The Voc is determined when the output current, I of the cell is zero (I=0)
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Vol. 8, No. 3, (2015)
whereas the Isc is determined when the output voltage, V of the cell is zero (V=0). The
maximum power is generated by the solar PV cell at a point of the V-I characteristic
where the product (V×I) is maximum. This point is known as the MPP and is unique.
Obviously, the two important factors which have to be taken into account in the
electricity generation of a solar PV panel are the irradiation and temperature. These
factors strongly affect the characteristics of solar PV panels. Consequently, the MPP
varies during the day. If the operating point is not close to the MPP, significant power
losses occur. Thus, it is essential to track the MPP in all conditions to ensure that the
maximum available power is obtained from the solar PV panel. This problem is
entrusted to the maximum power point tracking (MPPT) algorithms through searching
and determining MPPs in various conditions. This paper proposes the adaptive P&O
algorithm for searching MPPs which is presented in detail in next section.
3. Grid-Connected Solar Photovoltaic System
Solar PV systems can be divided into two types: standalone solar PV systems that
require a battery to store the energy and grid-connected solar PV systems used in high
power applications. The grid-connected solar PV system consists of main components
such as the solar PV array, DC/DC converter, DC/AC inverter, filter, transformer and
energy storage as shown in Figure 1.
PV Array
DC/DC MPPT
Converter
Energy Storage
DC/AC Inverter
Filter
Transformer
PWM
PLL
Power systems
Figure 1. Grid-Connected Solar Pv System
DC/DC converters are mainly used to either regulate the output voltage at a constant
value from a fluctuating power source to reduce the ripples in the output voltage or
achieve multiple voltage levels from the same input voltage. Several DC/DC
converters include the buck (step down), boost (step up) and buck-boost topologies.
Otherwise, DC/AC inverters are mainly used to convert a constant DC voltage into
three phase AC voltages with variable magnitude and frequency which are obtained by
controlling the semi-conductor switches with pulse width modulation (PWM)
techniques. The phase locked loop (PLL) is to provide the rotation frequency, direct
and quadrature voltage components at the point of common coupling by solving the
grid voltage abc components.
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4. MPPT Using an Advanced P&O Algorithm for a Grid-Connected
Solar PV System
It can be realized that the P&O algorithm generally uses a fixed step size which
results in a failure to track the MPP under fast varying atmospheric conditions. This
drawback can be overcome by using a variable step size under various atmospheric
conditions. This paper proposes the adaptive P&O algorithm.
It is assumed that the perturbation variable is the reference value for the solar PV
panel terminal voltage in this conventional P&O algorithm. Therefore, if the output
voltage of the solar PV panels is perturbed and dP/dV>0, then it is known that the
operating point is on the left side of the MPP. The P&O algorithm would increase the
solar PV panel reference voltage to move the operating point towards the MPP.
Alternatively, if the output voltage of the solar PV panels is perturbed and dP/dV<0,
then it is known that the operating point is on the right side of the MPP. The P&O
algorithm would decrease the solar PV panel reference voltage to move the operating
point towards the MPP. This description is shown more clearly in Figure 2 and Table 1.
The process is repeated periodically until the MPP is reached. Nevertheless, it can be
realized that the conventional P&O algorithm can fail under rapidly changes of various
atmospheric conditions as in Figure 3. It is started from an operating point, A. If the
atmospheric conditions are approximately constant, then a perturbation, V of the solar
PV voltage, V will move the operating point to B and the perturbation will be reversed
due to a decrease in power. However, if the solar irradiation increases and shifts the
power curve from P1 to P2 within one sampling period, the operating point will move
from A to C. This represents an increase in power and the perturbation is kept the same.
As a consequence, the operating point diverges from the MPP and will keep diverging
if the solar irradiation steadily increases [16]. In order to ensure that the MPPs are
tracked under sudden changes of the solar irradiation, an adaptive P&O algorithm is
proposed with a variable perturbation step size which depends on power changes. This
means that the perturbation step size varies and adapts continuously under varying
atmospheric conditions. The adaptive P&O algorithm is one of the conventional P&O
algorithm variants that can reduce the main drawbacks commonly related to the P&O
algorithm such as the convergence speed and tracking efficiency. The variable
perturbation step size which depends on power changes is given as follows.
 Vi   V0 
dP i
(5)
dV i
Ppv
dP/dV > 0
PMPP
dP/dV < 0
PMPP
VMPP
Vpv
Figure 2. Description of the Conventional P&O Algorithm
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Ppv
P2
C
P1
A
B
V+V
V
Vpv
Figure 3. Divergence of the Conventional P&O Algorithm from MPP
Begin
Measure: Vi, Ii
Calculate: Pi and ∆Vi
Yes
Pi=Pi-1
No
Yes
Yes
Vi>Vi-1
Vi+Vi
No
Pi>Pi-1
Yes
No
Vi-Vi
Vi>Vi-1
Vi+Vi
No
Vi-Vi
Return
Figure 4. Flow Chart of the Adaptive P&O Algorithm
Table 1. Summary of the Conventional P&O Algorithm
Perturbation
Power
Positive
Positive
Next perturbation
Positive
Positive
Negativ
Negative
Positive
Negative
Negativ
Positive
e
Negative
Negative
e
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5. Simulation Results
Simulation results are obtained by using MATLAB/SIMULINK software for the
MPPT control strategy of the solar PV system which is connected to the power system.
The solar PV system is configured by 20 panels in 10 series and 2 parallel
arrangements. The specifications and parameters of the solar PV system are shown in
Table 2. The grid voltage and frequency are 220 2 V and 50 Hz.
Figures 5 and 6 are the V-I and V-P characteristics of the solar PV system for
various solar irradiation values, G=1; 0.8 and 0.6 kW/m2 at the temperature, T=250C.
Figure 5 shows that the current of the solar PV panel increases when the solar
irradiation increases. Figure 6 shows the various MPPs at the various solar irradiations.
The MPPs are PMPP1=1000 W at G1=1 kW/m2; PMPP2=789 W at G2=0.8 kW/m2 and
PMPP3=581 W at G3=0.6 kW/m2. The adaptive P&O algorithm is proposed to determine
these MPPs under the various solar irradiations.
Figures 7 and 8 are the current and voltage of the grid-connected solar PV system
for the solar irradiation value, G=1 kW/m2 at the temperature, T=250C using the
conventional P&O algorithm. The current of the grid-connected solar PV system
reaches the steady state at t=0.365 s, Figure 7. However, it is also realized that the solar
PV system cannot track the MPP, PMPP=1000 W, this means that the conventional P&O
algorithm does not converge, Figure 9.
Figures 10 and 11 are the current and voltage of the grid-connected solar PV system
for the solar irradiation value, G=1 kW/m2 at the temperature, T=250C using the
adaptive P&O algorithm. The current of the grid-connected solar PV system reaches
the steady state at t=0.273 s and the solar PV system tracks the MPP, PMPP=1000 W,
Figure 12. This means that both the convergence value and speed are improved by
using the adaptive P&O algorithm.
There is a comparison between the powers obtained by the conventional and
adaptive P&O algorithms of the grid-connected solar PV system for the solar
irradiation value, G=1 kW/m2 at the temperature, T=250C, Figure 13. The comparison
confirms the effectiveness of the proposed P&O algorithm.
Furthermore, the solar irradiation is assumed to vary as follows: G1=0.8 kW/m2,
0st10.4s; G2=1 kW/m2, 0.4st20.8s and G3=0.6 kW/m2, 0.8st11s, Figure 14.
Figures 15 and 16 are the current and voltage of the grid-connected solar PV system
for the solar irradiation value varied shown in Figure 14, at the temperature, T=250C
using the conventional P&O algorithm. The current of the grid-connected solar PV
system reaches the steady state at t=0.455 s, Figure 15. However, it is also realized that
the solar PV system cannot track the MPPs, PMPP1, PMPP2 and PMPP3, this means that the
conventional P&O algorithm does not converge, Figure 17.
Figures 18 and 19 are the current and voltage of the grid-connected solar PV system
for the solar irradiation value varied shown in Figure 14, at the temperature, T=250C
using the adaptive P&O algorithm. The current of the grid-connected solar PV system
reaches the steady state at t=0.365 s and the solar PV system tracks the MPPs, PMPP1,
PMPP2 and PMPP3, Figure 20. This means that both the convergence value and speed are
improved by using the adaptive P&O algorithm.
There is a comparison between the powers obtained by the conventional and
adaptive P&O algorithms of the grid-connected solar PV system for the solar
irradiation value varied shown in Figure 14, at the temperature, T=250C, Figure 21. The
comparison confirms the effectiveness of the proposed P&O algorithm as well.
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Table 2. Specifications and Parameters of the Solar PV System
Parameter
Maximum
power,
Pmax (W)
Voltage at Pmax, VMPP
(V)
Current at Pmax, IMPP
(A)
Open-circuit voltage,
Voc (V)
Short-circuit current,
Isc (A)
PV
panel
50
PV
system
(10×2)
1000
17.4
174
2.875
5.75
21.42
214.2
3.11
6.22
8
Current, ipv (A)
G=1kW/m2
6
G=0.8kW/m2
G=0.6kW/m2
4
2
0
0
50
100
150
Voltage, vpv (V)
200
250
Figure 5. V-i Characteristic of the Solar PV System for Various Solar
Irradiation Values, g=1; 0.8 and 0.6 kw/m2 at the Temperature, t=250c
Figure 6. V-p characteristic of the solar pv system for various solar
irradiation values, g=1; 0.8 and 0.6 kw/m2 at the temperature, t=250c
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1.5
Current, iabc (A)
1
0.5
0
-0.5
-1
-1.5
0
0.3
0.35
0.4
Time, t (s)
0.45
0.5
Figure 7. Current of the Grid-Connected Solar PV System for the Solar
Irradiation Value, g=1 kw/m2 at the Temperature, t=250c using the
Conventional P&O Algorithm
Voltage, vabc (V)
400
200
0
-200
-400
0
0.3
0.35
0.4
Time, t (s)
0.45
0.5
Figure 8. Voltage of the Grid-Connected Solar PV System for the Solar
Irradiation Value, g=1 kw/m2 at the Temperature, t=250c Using the
Conventional P&O Algorithm
Power, ppv (W)
1000
800
600
400
200
0
0
0.2
0.4
0.6
Time, t (s)
0.8
1
Figure 9. Power of the Grid-Connected Solar PV System for the Solar
Irradiation Value, g=1 kw/m2 at the Temperature, t=250c Using the
Conventional P&O Algorithm
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Current, iabc (A)
2
1
0
-1
-2
0
0.3
0.35
0.4
0.45
0.5
Time, t (s)
Figure 10. Current of the Grid-Connected Solar PV System for the Solar
Irradiation Value, g=1 kw/m2 at the Temperature, t=250c Using the
Adaptive P&O Algorithm
Voltage, vabc (V)
400
200
0
-200
-400
0
0.3
0.35
0.4
Time, t (s)
0.45
0.5
Figure 11. Voltage of the Grid-Connected Solar PV System for the Solar
Irradiation Value, g=1 kw/m2 at the Temperature, t=250c Using the
Adaptive P&O Algorithm
1200
PMPP=1000W
Power, ppv (W)
1000
800
600
400
200
0
0
0.2
0.4
0.6
Time, t (s)
0.8
1
Figure 12. Power of the Grid-Connected Solar PV System for the Solar
Irradiation Value, g=1 kw/m2 at the Temperature, t=250c using the
Adaptive P&O Algorithm
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1200
Adaptive P&O algorithm
Power, ppv (W)
1000
800
600
Conventional P&O algorithm
400
200
0
0
0.2
0.4
0.6
Time, t (s)
0.8
1
Figure 13. Comparison between the Powers Obtained by the
Conventional and Adaptive P&O algorithm of the Grid-Connected Solar
PV System for the Solar Irradiation value, g=1 kw/m2 at the Temperature,
t=250c
Solar irradiation, G (kW/m2)
1.1
1
G=1kW/m2
0.9
0.8
G=0.8kW/m2
0.7
0.6
0.5
0
G=0.6kW/m2
0.2
0.4
0.6
Time, t (s)
0.8
1
Figure 14. Solar Irradiation at the Temperature, t=250c
Current, iabc (A)
2
1
0
-1
-2
0.3
0.4
0.5
0.6
0.7
Time, t (s)
0.8
0.9
1
Figure 15. Current of the Grid-Connected Solar PV System for the Solar
Irradiation Value Varied at the Temperature, t=250c using the
Conventional P&O Algorithm
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Vol. 8, No. 3, (2015)
Voltage, vabc (V)
400
200
0
-200
-400
0
0.75
0.8
0.85
0.9
Time, t (s)
0.95
1
Figure 16. Voltage of the Grid-Connected Solar PV System for the Solar
Irradiation Value Varied at the Temperature, t=250c using the
Conventional P&O Algorithm
Power, ppv (W)
800
600
400
200
0
0
0.2
0.4
0.6
Time, t (s)
0.8
1
Figure 17. Power of the Grid-Connected Solar PV System for the Solar
Irradiation Value Varied at the Temperature, t=250c using the
Conventional P&O Algorithm
Current, iabc (A)
2
1
0
-1
-2
0
0.4
0.5
0.6
0.7
Time, t (s)
0.8
0.9
1
Figure 18. Current of the Grid-Connected Solar PV System for the Solar
Irradiation Value Varied at the Temperature, t=250c using the Adaptive
P&O Algorithm
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Voltage, vabc (V)
400
200
0
-200
-400
0
0.75
0.8
0.85
0.9
Time, t (s)
0.95
1
Figure 19. Voltage of the Grid-Connected Solar PV System for the Solar
Irradiation Value Varied at the Temperature, t=250c using the Adaptive
P&O Algorithm
1200
PMPP=1000W
Power, ppv (W)
1000
PMPP=789W
800
600
PMPP=581W
400
200
0
0
0.2
0.4
0.6
Time, t (s)
0.8
1
Figure 20. Power of the Grid-Connected Solar PV System for the Solar
Irradiation Value Varied at the Temperature, t=250c using the Adaptive
P&O Algorithm
1200
Adaptive P&O algorithm
Power, ppv (W)
1000
800
600
400
Conventional P&O algorithm
200
0
0
0.2
0.4
0.6
Time, t (s)
0.8
1
Figure 21. Comparison between the Powers Obtained by the
Conventional and Adaptive P&O Algorithm of the Grid-Connected Solar
PV System for the Solar Irradiation Value Varied at the Temperature,
t=250c
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6. Conclusion
In this paper, the MPPT control strategy of a solar PV system has been proposed by
using an adaptive P&O algorithm. The proposal utilized the variable perturbation step
size which depends on various atmospheric conditions. The obtained simulation results
are compared with those using the conventional P&O algorithm, which show the
effectiveness of the proposed MPPT control strategy for a grid-connected solar PV
system.
References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
G. M. Master, “Renewable and efficient electric power systems”, A John Wiley & Sons, Inc.,
Publication, (2004), pp. 385-604.
R. Faranda and S. Leva, “WSEAS Trans. Power Syst.”, Energy comparison of MPPT techniques for
PV systems, vol. 3, iss. 6, (2008), pp. 446-455.
R. Sridhar, S. Jeevananthan, N. T. Selvan and P. V. S. Chowdary, “Performance improvement of a
photovoltaic array using MPPT P&O technique”, IEEE Int. Conf. Commun. Control and Comput.
Technol., ICCCCT, (2010).
N. M. Razali and N. A. Rahim, “DSP-based maximum peak power tracker using P&O algorithm”,
IEEE First Conf. Clean Energy and Technol., CET 2011 (2011).
D. C. Huynh, T. A. T. Nguyen, M. W. Dunnigan and M. A. Mueller, “Maximum power point
tracking of solar photovoltaic panels using advanced perturbation and observation algorithm”, 8th
IEEE Conf. Ind. Elect. and Applicat., ICIEA2013 (2013).
B. Liu, S. Duan, F. Liu and P. Xu, “Analysis and improvement of maximum power point tracking
algorithm based on incremental conductance method for photovoltaic array”, 7th Int. Conf. Power
Electron. and Drive Syst., PEDS2007, (2007).
W. Ping, D. Hui, D. Changyu and Q. Shengbiao, “An improved MPPT algorithm based on traditional
incremental conductance method”, 4th Int. Conf. Power Electron. Syst. and Applicat., PESA 2011,
(2011).
D. C. Huynh, “Int. J. Science and Research, An improved incremental conductance maximum power
point tracking algorithm for solar photovoltaic panels”, vol. 3, no. 10, (2014), pp. 342-347.
Y. Zhihao and W. Xiaobo, “Compensation loop design of a photovoltaic system based on constant
voltage MPPT”, Asia-Pacific Power and Energy Eng. Conf., APPEEC2009, (2009).
K. A. Aganah and A. W. Leedy, “A constant voltage maximum power point tracking method for
solar powered systems”, IEEE 43rd Southeastern Sym. Syst. Theory, SSST2011, (2011).
S. J. Kang, J. S. Ko, J. S. Choi, M. G. Jang, J. H. Mun, J. G. Lee and D. H. Chung, “A novel MPPT
control of photovoltaic system using FLC algorithm”, 11th Int. Conf. Control, Automat. and Syst.,
ICCAS2011, (2011).
V. Padmanabhan, V. Beena and M. Jayaraju, “Fuzzy logic based maximum power point tracker for a
photovoltaic system”, Int. Conf. Power, Signals, Controls and Comput., EPSCICON2012, (2012).
R. Ramaprabha, V. Gothandaraman, K. Kanimozhi, R. Divya and B. L. Mathur, “Maximum power
point tracking using GA-optimized artificial neural network for solar PV system”, 1st Int. Conf. Elect.
Energy Syst., ICEES2011, (2011).
Md. A. Azam, S. A. A. Nahid, M. M. Alam and B. A. Plabon, “Microcontroller based high precision
PSO algorithm for maximum solar power tracking”, Int. Conf. Inform., Electron. and Vision,
ICIEV2012, (2012).
K. Ishaque, Z. Salam, M. Amjad and S. Mekhilef, “IEEE Trans, Power Electron”, An improved
particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillation, (2012),
pp. 3627-3638.
T. Esram and P. L. Chapman, “IEEE Trans, Energy Convers”, Comparison of photovolatic array
maximum power point tracking techniques, vol. 22, no. 2, , (2007), pp. 439-449.
Copyright ⓒ 2015 SERSC
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International Journal of Grid Distribution Computing
Vol. 8, No. 3, (2015)
Authors
Duy C. Huynh, He received the B.Sc. and M.Sc. degrees in
electrical and electronic engineering from Ho Chi Minh City
University of Technology, Ho Chi Minh City, Vietnam, in 2001
and 2005, respectively and Ph.D. degree from Heriot-Watt
University, Edinburgh, U.K., in 2010. In 2001, he became a
Lecturer at Ho Chi Minh City University of Technology. His
research interests include the areas of energy efficient control and
parameter estimation methods of induction machines and
renewable sources.
Matthew W. Dubbigan, He received his B.Sc. in Electrical
and Electronic Engineering (with First-Class Honours) from
Glasgow University, Glasgow, U.K., in 1985 and his M.Sc. and
Ph.D. from Heriot-Watt University, Edinburgh, UK, in 1989 and
1994, respectively. He was employed by Ferranti from 1985 to
1988 as a Development Engineer in the design of power supplies
and control systems for moving optical assemblies and device
temperature stabilisation. In 1989, he became a Lecturer at
Heriot-Watt University, where he was concerned with the
evaluation and reduction of the dynamic coupling between a
robotic manipulator and an underwater vehicle. He is currently a
Senior Lecturer, Associate Professor and his research grants and
interests include the areas of hybrid position/force control of an
underwater manipulator, coupled control of manipulator-vehicle
systems, nonlinear position/speed control and parameter
estimation methods in vector control of induction machines,
frequency domain self-tuning/adaptive filter control methods for
random vibration, and shock testing using electro-dynamic
actuators.
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