Systems Engineering and Innovation in Control
Control—an
an
Industry Perspective and an Application to Automotive
Powertrains
Tariq
q Samad
Corporate Fellow, Honeywell Automation and Control Solutions
In collaboration with Greg Stewart, Honeywell
College Park, Maryland, 28 Oct. 2013
 Honeywell.com
Outline
•
•
•
•
•
•
Honeywell and controls
Advanced control applications in the industrial context
Trends in automotive powertrain control
Advanced control for p
powertrains—initial “successes”
Advanced control for powertrains—Honeywell OnRAMP
Summary and conclusions
2
Document control number
 Honeywell.com
Honeywell’s
Honeywell
s Businesses
• $37.5 billion in revenues, about 55% of sales outside of U.S.
• More than 130,000 employees, operating in more than 100 countries
Automation and
Control Solutions
3
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Aerospace
Performance
Materials and
T h l i
Technologies
Transportation
Systems
 Honeywell.com
A Brief History of Honeywell Controls
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Albert Butz invents “damper flapper”, forms company, 1885
Minneapolis-Honeywell Regulator Company formed, 1927
Acquisition of Brown Instrument Co.,
Co entry into process control
control, 1934
Minneapolis-Honeywell C-1 Automatic Pilot put into production, 1943
T-86 “RoundTM” thermostat introduced, 1953
Honeywell Research Center established: “Research must always be relevant to the field of automatic
control ” 1958
control,
First computer-based control system for a process plant, 1961
Delta 2500, computer control system for buildings, introduced in 1971
Honeywell introduces TDC 2000, first distributed control system (DCS) in 1975
Fi t flight
First
fli ht managementt system
t
introduced
i t d
d (B757,
(B757 Sperry
S
acquisition),
i iti ) 1982
Foundational developments in robust control, early 1980s
Allied-Signal merger, 2000—automotive, engines, and specialty materials businesses
New MPC developments, 2000s: nonlinear, explicit, embedded, distributed
New applications for advanced control, 2000s: microgrids, automotive, supply-chain management, water
distribution networks
Controls-related acquisitions: Invensys Sensors, PAS, Akuacom, Matrikon, others
4
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J.L. Rodengen, The Legend of Honeywell, Write Stuff Syndicate, 1995
 Honeywell.com
Honeywell Presence in Advanced Controls
Industry
Example Applications
Realized Benefits
Oil Refining
Petrochemicals
Oil and Gas
Refinery, Ethylene Plant,
Aromatics, Xylene, Gas
Processing, LNG/LPG
• 2-15% higher production
• Refinery:
R fi
~$1/Barrel
$1/B
l ffor advanced
d
d control
t l
• 5-20% less energy/unit product
Pulp &
Paper
Cross/Machine Directional
Control
• Up to 50% higher performance
• 50-80% lower calibration time
B ildi Control
Building
C t l
HVAC adaptive
d ti control
t l
• 7-33%
7 33% energy costt savings
i
• Low setup costs
Commercial Aircraft
B787, C919
EPIC, APEX
• Stabilization of unstable aircraft
• Level 1 handling qualities
Aero Engines
AS907-1
HTF 7500E
HPW3000
• 99.7% fault coverage
• Optimized engine start
• Improved engine life with power assurance
Space
Orion Multi-Purpose Crew Vehicle
• reduced propellant requirements by 20%
• optimal steering of Control Moment Gyro
Militaryy &
Unmanned Aircraft
Reusable Launch Vehicle,, T-Hawk
• Stabilization, Vehicle Utilityy & Operability
p
y
• 4X less development time
• Missions completed after component failures
• Problem dimensions up to 1000s of measurement points, 100s of actuators
y
from milliseconds to minutes
• Dynamics
30+ years of advanced control leadership and successful products
5
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 Honeywell.com
Outline
•
•
•
•
•
•
Honeywell and controls
Advanced control applications in the industrial context
Trends in automotive powertrain control
Advanced control for p
powertrains—initial “successes”
Advanced control for powertrains—Honeywell OnRAMP
Summary and conclusions
6
Document control number
 Honeywell.com
Advanced Control – Industry
Industry-specific
specific Considerations
•
•
•
•
•
•
•
Value chain: who does the control design, software development, integration?
How many identical copies of a controller will be deployed (one to millions)?
How easy or difficult is it to “adjust” a fielded control algorithm?
What variety of conditions will be encountered in practice?
Is the application safety critical?
What is the expected lifetime of the application?
What regulatory and certification requirements must be addressed?
7
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 Honeywell.com
Advanced Control – End-to-End, Systems
E i
Engineering
i Perspective
P
ti
• In the business context, advanced control isn’t just about the
algorithm
l ith . . .
• Numerous other factors are relevant
–
–
–
–
technical
industry sector
work process and environment—including people involved
benefits—vis-à-vis application-specific requirements
• Understanding the “big
big picture
picture” is crucial when considering new
control technology
T. Samad and G. Stewart, “Perspectives on innovation in control systems technology: Compatibility with
industry practices,” IEEE Trans. on Control Sys. Tech., Mar. 2013.
8
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 Honeywell.com
Advanced Control – Technical Considerations
– Plant: nonlinear,
nonlinear multivariable
multivariable, constraints
constraints, dynamics
dynamics,
uncertain, time-varying
– Sensors and actuators: presence, performance,
reliabilityy
– Computing and communications platform: memory
(RAM, ROM), processor power (clock rate, floating/fixed
point, DSP), networks (wireless, wired, protocols)
– SW structure and processes: legacy control code,
software development methodology
– Control tuning: objective versus subjective
– Tooling:
T li
application
li ti software
ft
f designers,
for
d i
d
developers,
l
operators, engineers
Requirements
Modeling
Control
Design
V&V/
Certification
Controller
Deployment
Maintenance
9
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 Honeywell.com
Simplified value chains for control algorithms
Algorithm
developer
Software
implementer
Aerospace
Control
system
supplier
OEM
Airline or
leasing
company
University research
group or in-house R&D
Controls company or
third-party application
house
“Controls company”
(Collins, Honeywell,
Thales, …)
Aircraft manufacturer
(Airbus, Boeing,
Bombardier, Embraer,
…)
Algorithm
developer
University research
group or in-house R&D
Software
implementer
Controls company or
third-party application
house
Control
system
supplier
“Controls company”
(ABB, Emerson,
Honeywell, Invensys,
Siemens, Yokogawa,
…)
EPC
(AMEC, Bechtel, Fluor,
FosterWheeler,
Samsung Eng.,
WorleyParsons, …)
Process
plant
owner
(ExxonMobil, Shell,
Reliance India,
Sinopec, Weyerhauser,
…)
Process industry
Complexities not considered include retrofit applications
applications, the roles of other organizations such as suppliers
of other systems, standards and regulatory bodies, and financiers. Value chains for other controls
technology developments, such as control design tools, will be different.
10
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Differentiating Control
A li ti
Applications—Examples
E
l
Engine and Desired
Performance
Steady-State Engine
Calibration
Integration
(testing and debugging)
Calibration
(simulation, testbench,
vehicle)
Software Coding
Software and
Control Testing
Development
Iterations
Post-Dep
ployment Iteration
ns Including
Deve
elopment of New V
Versions
Software Development
(specification, testbench,
vehicle)
Short Path
n
Functional Iteration
Functional Testing
((simulation,
i
l ti
t tb
testbench,
h
vehicle)
Software Iterration
Long Path
Fu
unctional Iteration
Control Functional
Development
Control Functional
Development
Control Product Release
On-Site Commissioning
(model process, configure
and tune control)
Paper Machine and
Desired Performance
Post-Commissioning
P
C
i i i
Maintenance
Papermaking Control Development Process
Certification and Release
Engine Control Development Process
11
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Stewart and Samad, in Impact of Control
Technology, ieeecss.org/main/IoCT-report
 Honeywell.com
The “So
So what?
what?” of advanced control
• Benefits typically a combination of
–
–
–
–
–
–
accelerated development time
time—design,
design development
development, calibration,
calibration testing
testing, . . .
enhanced insight or simplified development process
system performance in normal operating conditions
robust p
performance to p
product variations and in off-nominal conditions
reliability and fault tolerance
reduced cost of hardware
• And these must all be considered in context
– . . . relative to current solutions and alternatives
– . . . given the current business and technical environment
12
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 Honeywell.com
Notable Advantages of PID Controllers
•
•
•
•
•
•
•
Modeling not required
PhD’s not required
Easy to install and commission
Easy
y to adjust
j
the controller during
g operation
p
Familiar to control engineers and operators
Design and implementation processes already established
Computationally and algorithmically simple
Advanced control benefits must be sufficient to overcome
these advantages . . . And there are many such successes!
13
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 Honeywell.com
Outline
•
•
•
•
•
•
Honeywell and controls
Advanced control applications in the industrial context
Trends in automotive powertrain control
Advanced control for p
powertrains—initial “successes”
Advanced control for powertrains—Honeywell OnRAMP
Summary and conclusions
14
Document control number
 Honeywell.com
Large-scale
Large
scale vs Embedded Systems Engineering
• Much academic research in SE focused on large-scale programs
(
(e.g.,
aerospace and
dd
defense
f
platforms)
l tf
)
• Many control applications in commercial and industrial
applications and devices
• Iterative, agile product development
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 Honeywell.com
Challenges
C
a e ges Facing
ac g tthe
e Transportation
a spo tat o Industry
dust y
• Industry Spent >$1B on Control Design & Calibration in 2011
• Lines of Control Code are Increasing by Factor of 10 every 8 Years
• Development Cost for Software will Exceed Hardware before 2020
• Controls are being Developed using a Non-Systematic Approach
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 Honeywell.com
Global Trends Necessitating Advanced Controls
30
• Increasing complexity of engines
Number
– more components,
components more actuators and
sensors – increasing development cost
– control scope increase: emerging
sophisticated combustion technologies and
subsystem interaction
– complexity brings new combinations of
operating conditions and failure modes
25
number sensors
number actuators
20
15
10
5
1990
1995
2000
Year
2005
2010
• Increasingly tighter requirements
–
–
–
–
–
emissions legislation
fuel efficiency
performance
reliability
cost
Europe
17
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U.S.
 Honeywell.com
Turbocharged
g engines
g
Single stage
Serial dualstage
Engine
Parallel dual-stage*
EGR
Engine
T
C
HP
EGR
3-Way
Valve/by
pass
valve
T
C
LP
C
shaft
T
Cat
+ DPF
Air filter
* EGR not illustrated
• Many configurations; all MIMO
MIMO, all nonlinear
• Even the simple engine structure is well known to pose control challenges.
18
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 Honeywell.com
Air induction control loops in
( i l ) tturbocharged
(simple)
b h
d
engines
Boost
pressure
sensor
Electronic control unit
PID
PID
Air flow
sensor
Variable
nozzle
vane position
Exhaust gas
g
recirculation valve
19
Highly nonlinear engine often controlled by combination of
Image from Garrett Presentation, UW, 2003
lookup tables and PID controllers
Document control number
 Honeywell.com
Automotive versus process control
Processor Speed
3000
Memory
35
2.83 GHZ
2500
30
32 GB
25
2000
20
1500
15
1000
10
500
5
2-4 MB
40-56
0 56 MHZ
0
0
automotive
process
industry
automotive
process
industry
Control algorithm must have small CPU and memory footprint
. . . a challenge for model-based control?
20
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 Honeywell.com
Outline
•
•
•
•
•
•
Honeywell and controls
Advanced control applications in the industrial context
Trends in automotive powertrain control
Advanced control for p
powertrains—initial “successes”
Advanced control for powertrains—Honeywell OnRAMP
Summary and conclusions
21
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Linear Model Predictive Control
Linear Model Predictive Control
22
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 Honeywell.com
 Honeywell.com
Fast MPC (Borrelli, 2003)
Multiparametric technology: Recent developments in
advanced control allow dramatic reduction in computational
complexity; control is much easier to verify and implement
Solution of a constrained optimization problem for finding a
series of control moves:
min J U , x(0) 
U
subject to g (U , x(0))  0
where cost function J and constraints g are determined from
a model of the system and control requirements. Constraints
and criteria are specified during control design.
The optimization is solved offline with a math program
solver, generating a simple online implementation:
U *  f 0 x(0)   Fi x(0)  Gi if x(0)  Di
23
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Several extensions, including for
multi-mode
u t ode systems
syste s (colors:
(co o s modes,
odes,
segments: different Di)
1/3
 Honeywell.com
Setpoints: Boost and MAF
Actuators: EGR valve and VGT
vanes
MAP
MAF
setpoint setpoint
speed
fuel
Boost [%]
B
Multivariable Control over NEDC-like cycle
100
75
50
0
20
40
60
80
100
120
140
160
180
200
0
20
40
60
80
100
120
140
160
180
200
0
20
40
60
80
100
120
140
160
180
200
0
20
40
60
80
100
120
140
160
180
200
0
20
40
60
80
100
120
140
160
180
200
100
MAF [%]
M
Engine: small engine with single
turbo and EGR
Experiment: simultaneous tracking
of setpoints through changing
engine speed and load transients.
50
0
VGT
H-ACT
controller
MAF sensor
EGRv
100
NOx [%]
N
MAP sensor
75
50
25
100
fuel
VGT [%]
V
Transient: NEDC-like
80
60
20
40
60
80
100
120
140
160
180
200
speed
0
0
20
40
60
80
100
time
120
140
160
180
200
EGR
R valve [%]
40
100
50
0
Time [seconds]
24
Implemented on a production ECU on a production engine
Document control number
2/3
 Honeywell.com
Control Over Highly Transient Part Of FTP Cycle
Controller: MAP control with timevarying turbospeed and VGT
constraints, using VGT actuator
MAP sensor
turbospeed sensor
0
100
200
300
Time [sec]
400
500
600
0
100
200
300
Time [sec]
400
500
600
0
100
200
300
Time [sec]
400
500
600
0
100
200
300
Time [sec]
400
500
600
100
50
0
100
speed
fuel
H-ACT
controller
Fuel [%]
MAP
setpoint
50
0
VGT [[%]
Engine: Medium size engine with
single turbo and EGR
Experiment: Modes 2 and 3 of FTP
cycle
MAP [%]
M
100
50
0
VGT
Turbospeed
constraint
VGT
constraint
Sp
peed [%]
100
50
0
25
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Control with time-varying actuator constraints
3/3
 Honeywell.com
Press
Flow
NOx
setpoint setpoint constraint
Press sensor
VGT
H ACT
H-ACT
controller
Flow sensor
NOx sensor
EGRv
2050
2000
0
1200
50
100
150
200
250
50
100
150
200
250
50
100
150
200
250
50
100
150
200
250
50
100
150
200
250
1100
NOx [ppm]
1000
0
500
400
300
0
90
VGT [%]
Setpoints:
S
t i t Pressure
P
and
d Flow
Fl
Actuators: EGR valve and VGT
vanes
Constraint: engine-out NOx
2100
EG
GR valve [%]
• Augmented with
constraint on
engine-out NOx
flo
ow [mg/cyc] b
boost [hPa]
Engine
g e Co
Control
t o Within
t
Emissions
ss o s Co
Constraint
st a t
80
70
0
100
50
0
time [seconds]
26
Model-based control: flexibility in problem formulation
Document control number
 Honeywell.com
We thought we were done, but . . .
• Customers very impressed with our controls expertise and test
cellll demonstrations!
d
t ti
!
– not just the performance achieved, but the speed of controller
development
• But
B t then
th questions
ti
started
t t d to
t come up . . .
– what does this really mean for us?
– how will it fit within our processes?
. . . our learning experience was just starting.
27
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 Honeywell.com
Outline
•
•
•
•
•
•
Honeywell and controls
Advanced control applications in the industrial context
Trends in automotive powertrain control
Advanced control for p
powertrains—initial “successes”
Advanced control for powertrains—Honeywell OnRAMP
Summary and conclusions
28
Document control number
 Honeywell.com
Overview of Standard Control Development
p
Process
Engine and steadystate calibration
Control
development
Resulting controller must perform well
for all engines and over lifetime of fleet.
fleet
Release
20
Post-release
support
29
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…
…
Certification
…
Calibration (tuning)
off controllers
t ll
…
…
0
tim
me [years
s]
Software
development
 Honeywell.com
OnRAMP – Optimized, Model-Based
Model Based Design
Modeling
Control
Design
Controller
Deployment
Ph i Based
Physics
B
d Model
M d l
Control Design
Deployment
Measured Data
Optimal
M lti i bl
Multivariable
Control
30
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 Honeywell.com
Engine Development w/ OnRAMP
Hardware Selection
Try New HW
Production Software
& Emissions Cal
OBD Software & Cal
Design Control
…
Maturing Calibration
Test over Drive Cycle
0%
(~2-3 Years)
100%
Traditional
OnRAMP
More Chances to Get Hardware
Right in Less Time
• Faster Iterations
• Coordinated AFT Controls
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Intuitive Tradeoffs Between Driveability
and Emissions/Fuel Economy  More
Mature Cal in Same Time, or Launch
Early and Earn Credits
Reduce Variation
• Faster OBD Val
• Fewer Returns from Field
 Honeywell.com
A New Approach to Powertrain Control
Tomorrow with
OnRAMP
Today’s Control
F l Injection
Fuel
I j ti Control
C t l
Fuel Injection Control
Engine Brake Control
Engine Brake Control
Turbo Control
EGR Control
Intake Throttle Control
Exhaust Throttle Control
Start of Injection Control
Fuel Rail Pressure Control
Actua
ators
Sens
sors
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OnRAMP
Multi-Input /
Multi-Output
Air Induction
&
Aftertreatment
C
Control
SCR Control
DOC/Regen Control
Complex, Labor Intensive
32
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Simplified, Streamlined
 Honeywell.com
OnRAMP: Model Setup
Modeling
Air Filter
Intake
Manifold
Compressor CAC
EGR Mixing
EGR
Cooler
EGR
Valve
Control
Design
VGT
Turbine
Controller
Deployment
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Engine
Exhaust
Manifold
• Engine Layout Constructed Piece-by-Piece
from a Library of Components
 Honeywell.com
Control Oriented Modeling (COM)
Model Structure
Engine and Component Data
Modeling
Automatic Model Fit
Control
Design
Data = BLUE, Unweighted = RED
5
4.5
Component
Level Fit
4
PRC [--]
3.5
Controller
p y
Deployment
3
2.5
2
1.5
1
34
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0
0.1
0.2
0.3
0.4
WC [kg/s]
0.5
0.6
0.7
 Honeywell.com
Control Oriented Modeling (COM)
Model Structure
Engine and Component Data
Modeling
Control
Design
Automatic Model Fit
Compressor flow
Controller
p y
Deployment
35
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Compressor flow
Global
Gl b l
Model
Fit
 Honeywell.com
OnRAMP: Control Design
Boost Pressure
Air Flow
Modeling
Turbospeed
EGR
Valve
Control
Design
VGT
Vanes
Controller
Deployment
36
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User Specifies:
• Actuators
• Sensors
• Setpoints
• Constraints
 Honeywell.com
OnRAMP tools
• Ordinarily configuring an engine
model in Simulink takes around a
week and is error p
prone
• With OnRAMP, the user can drag and
drop engine components and a
patented routine automatically
generates the wiring. This requires
10-30 minutes and significantly
reduces the opportunity for model
configuration errors.
• Developed for users without
multivariable control background
• Slider bars allow MIMO tradeoffs
• Automatic tuning algorithm
determines MPC and observer
weights to satisfy small-gain robust
stability condition
37
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OnRAMP: Systematic procedure for advanced control
d i and
design
d iimplementation
l
t ti
Configure Model
Calibrate Model
Specify
Control Problem
• Component
p
libraries for developing
p g control-oriented
models
– low-order models that capture the essential physics
– nonlinear ODEs generated for control synthesis
– modeling tool must be robust for all users and engines
• Model calibration as nonlinear identification
• Feedforward and feedback control derived from model
• General ECU template permits many controller
configurations
– no software structure changes
S th i Control
Synthesize
C t l
Implement
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• MIMO controller integrates into production software
hierarchy
 Honeywell.com
minU
Controller Synthesis
J (U ; x(t ))   yˆ (t  k | t )  u (t  k )
subject to
Ny
2
2
k 0
Q
R
umini  u (t  k )  u max
ymin  yˆ (t  k | t )  ymax
Resulting real time controller:
• Calibration data {Fj, Gj, Hj, Kj} are problem specific, but
• Algorithm structure does not change
Behind the scenes for the user: fast MPC with patented extensions
39
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H-ACT:
H
ACT: Design Steps
Trading off CPU Time and
Memory in MPC
Configure Model
[Borrelli, Baotic, Pekar, Stewart, ECC09]
explicit
li i MPC
Specify
S
if
Control Problem
memory
Calibrate Model
Numerically stable primal-dual
feasibility (NSPDF) algorithm
MPC via active
active-set
set QP
Synthesize Control
number of operations per fixed active constraints
Implement
40
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OnRAMP – Modeling, Control & Calibration
Cycle
y
Time
Reduction
Fuel Efficiency /
Emissions
Warranty
y
Reduction
• Transient Control on Engine in < 2 weeks
• Ability to Reconfigure Structure in < 1 Day
• > 1 FTE Annual Savings per License
• > 2% Fuel Efficiency Improvement Projected*
• Up to 70% Reduction in Engine Out Smoke
• Robust to Engine / Aftertreatment Ageing
• > 50% Reduction in Actuator Activity
• Remove
R
P
Potential
t ti l for
f Actuator
A t t “Fighting”
“Fi hti ”
• Potential to reduce # of Sensors on Engine
Several applications by engine manufacturers . . . clean
clean-sheet
sheet development time for
transient control reduced in most cases from several months to a few weeks.
41
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 Honeywell.com
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http://www.honeywellonramp.com
 Honeywell.com
Outline
•
•
•
•
•
•
Honeywell and controls
Advanced control applications in the industrial context
Trends in automotive powertrain control
Advanced control for p
powertrains—initial “successes”
Advanced control for powertrains—Honeywell OnRAMP
Summary and conclusions
43
Document control number
 Honeywell.com
Advanced control in context
Computing
platform
Actuators
Sensors
Control
algorithm
g
design
Plant understanding
Modeling
Value chain
Domain
(design flow,
people)
Control algorithm design cannot be isolated from its intended environment
44
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OnRAMP—Advanced Control for Powertrains
O
Overview:
i
a development process and software tool for automatic generation of models and
optimal control algorithms for a wide range of engine applications
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Model structure
User-built from
from
User-built
OnRAMP
component
H-ACT component
library
library
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Control
C
ld
design
i i tool
Ht ACT
H-ACT
d
design
t l
RP System or ECU
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Engine and component data
• Modeling: tool for configuration and automated robust identification of
nonlinear grey box engine model to fit input-output data.
• Control: “explicit
explicit MPC”
MPC technique tailored for implementing nonlinear MPC in a
production ECU environment.
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 Honeywell.com
Advanced Control Applications as Systems Engineering
• Understanding the domain and the industry
– how are the problems addressed today?
– how is control performed in the industry and by whom?
• Understanding requirements and the reasons for them
– what’s the “so what?”—from the end user to the immediate customer?
• Understanding if/how advanced control can be a solution
– what are the barriers to change
g that must be overcome?
– what are the costs and benefits versus the “next-best alternative”?
• Tools for modeling, control design, deployment, and support
– how can advanced control be systematic, replicable, scalable?
Req irements dri en model
Requirements-driven,
model-based,
based agile control design !
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Document control number
 www.HoneywellOnRAMP.com
The Impact of Control
T h l
Technology
• www.ieeecss.org/main/IoCT-report
(T. Samad & A. Annaswamy, eds., 2011)
• 40+ two-page flyers
– “Success stories for control”
– “Grand challenges for control”
• 2nd edition in preparation (2014)
– 70 – 80
80+ flyers expected
– solicitations welcome through end of year
Highlighting real-world accomplishments and opportunities in our field!
47
Honeywell Proprietary
 www.HoneywellOnRAMP.com
Questions?
Tariq Samad
Honeywell
+1 763 954 6349
tariq.samad@honeywell.com
48
Honeywell Proprietary