Parametric Programming & Control

Report
University Politehnica of
Bucharest Doctor Honoris Causa
Professor Stratos Pistikopoulos FREng
Outline
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A brief introduction
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Chemical Engineering
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Process Systems Engineering
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On-going research areas & projects

Multi-parametric programming & control
Stratos Pistikopoulos
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Diploma (Chem Eng) AUTh, 1984
PhD (Chem Eng) CMU, 1988
1991 – Imperial College London; since 1999 Professor of
Chemical Engineering
2002 - 2009 Director, Centre for Process Systems
Engineering (CPSE), Imperial
2009 - 2013 Director of Research, Chem Eng, Imperial
2009 - 2013 Member, Faculty of Engineering Research
Committee, Imperial
Stratos Pistikopoulos
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Process systems engineering
Modelling, optimization & control
Process networks, energy & sustainable systems,
bioprocesses, biomedical systems
250+ major journal publications, 8 books, 2 patents
h-index 40; ~5000 citations
Stratos Pistikopoulos
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FREng, FIChemE
(Co-) Editor, Comp & Chem Eng
Co-Editor, Book Series (Elsevier & Wiley)
Editorial Boards – I&ECR, JOGO, CMS
Founder/Co-founder & Director – PSE Ltd, ParOS
2007 – co-recipient Mac Robert Award, RAEng
2008 – Advanced Investigator Award, ERC
2009 – Bayer Lecture, CMU
2012 – Computing in Chemical Engineering Award, CAST,
AIChE
2014 – 21st Professor Roger Sargent Lecture, Imperial
Chemical Engineering
Emerging Chemical Engineering

Relatively young[er] profession (societies founded in early part
of 19th century, Manchester, UCL, Imperial - 1880s; MIT 1888)

(Most likely the) most versatile engineering profession
(strong societies & academic programmes, highly-paid in
manufacturing, business, banking, consulting)
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Central discipline towards addressing societal grand
challenges (energy & the environment/sustainability, health & the
bio-(mics) ‘revolution’, Nano-engineering, Info-’revolution’, central to
almost all Top 10 emerging technologies for 2012 World Economic
Forum!)

Multi-scale & multi-discipline chemical engineering
Evolution of Chemical Engineering
Recognition of length and time scales
Evolution of Chemical Engineering
Factors
Length-scale
Energy
(algae, energy-based metabolic engineering
& optimisation)
Transport
(Molecular
Design of
Nanoparticles)
Product
(quality, formulation,
quantity)
Control
(model-based
Information pathways)
Time-scale
Only Chemical Engineering integrates TIME, LENGTH, FACTORS (input/output)
Chemical Engineering - research

Research .. – strong core chemical engineering, new
opportunities in nano-driven chemical engineering, biochemical and
biomedical-driven chemical engineering, energy/sustainability-driven
chemical engineering, info-driven chemical engineering

Interactions/interfaces with chemistry, materials,
medicine, biology, computing/applied math & beyond –
molecular level, nano-materials, nano/micro-reaction, ‘micro-human’,
carbon dioxide conversion, bio-energy, resource efficiency & novel
manufacturing, from ‘mind to factory’, systems of systems, ...
Chemical Engineering – a model
Core
Multi-scale
Understanding
& Modelling
Chemical Engineering – a model
Design/
Products &
Processes
Core
Multi-scale
UnderstandingMeasurements/
Experiments/
& Modelling Visualization/
Validation
Analytics
Properties/
Transport/
Reaction/
Separation
Simulation/
Optimization
Chemical Engineering – a model
Molecular &
Materials/Product
Chemical Engineering
Bio & Medical
driven
Chemical
Design/
Simulation/
Engineering
Products &
Optimization
Processes
Core
Multi-scale
UnderstandingMeasurements/
Experiments/
& Modelling Visualization/
Validation
Analytics
Properties/
Transport/
Energy/
Nano-Chemical
Reaction/
Sustainability
Engineering
Separation
Chemical
Engineering
Chemical Engineering – a model
Molecular/Materials
Chemical Engineering
Materials
Systems
Bio & Med
driven
Chemical
Engineering
Core
Multi-scale
Reaction Understanding
Analytical
& Modelling
&
Sciences
Catalysis
Nano- &
Multi-scale
Chemical
Engineering
Transport
&
Separation
Energy/
Sustainability
Chemical
Engineering
Outline

A brief introduction

Chemical Engineering
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Process Systems Engineering
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On-going research areas & projects

Multi-parametric programming & control
Process Systems Engineering
Process Systems Engineering

Scientific discipline which focuses on the ‘study &
development of theoretical approaches, computational
techniques and computer-aided tools for modelling, analysis,
design, optimization and control of complex engineering &
natural systems – with the aim to systematically generate and
develop products and processes across a wide range of
systems involving chemical and physical change; from
molecular and genetic information and phenomena, to
manufacturing processes, to energy systems and their
enterprise-wide supply chain networks’
PSE – brief historical overview

Relatively ‘new’ area in chemical engineering –
started in the sixties/early seventies [Roger Sargent, Dale
Rudd, Richard Hughes, and others & their academic trees]

Chemical Engineering – around 1890+ [MIT, UCL, Imperial]

AIChE - 1908; IChemE - 1922
PSE – brief historical overview

Relatively ‘new’ area in chemical engineering –
started in the sixties/early seventies [Roger Sargent, Dale
Rudd, Richard Hughes, and others & their academic trees]

Key historical dates – 1961 the term introduced
[special volume of AIChE Symposium Series]; 1964 first
paper on SPEEDUP [simulation programme for the
economic evaluation and design of unsteady-state
processes]; 1968 first textbook ‘Strategy of Process
Engineering’ by Rudd & Watson (Wiley); 1970 CACHE
Corporation; 1977 CAST division of AIChE; 1977
Computers & Chemical Engineering Journal
PSE – brief historical overview
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1980s – FOCAPD 1980; PSE 1982; CPC, FOCAPO
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Early 90s – ESCAPE series
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Significant growth
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Centres of excellence & critical mass – CMU,
Purdue, UMIST, Imperial, DTU, MIT, others around
the world (US, Europe, Asia – Japan, Singapore, Korea,
China, Malaysia)
PSE – Current Status

Well recognized field within chemical engineering

PSE academics in many [most?] chemical
engineering departments

Undergraduate level – standard courses [& textbooks]
on process analysis, process design, process control,
optimization, etc

Research level – major activity & strong research
programmes [US & Canada, Europe, Asia, Latin America,
Australia]
PSE – Current Status

Well established global international events &
conferences
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Highly respected journals, books & publications
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Strong relevance to & acceptance by industryacross wide range of sectors [from oil & gas to
chemicals, fine chemicals & consumer goods, ..]
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PSE software tools – essential in industry &
beyond [simulation, MPC, optimization, heat integration,
etc – PSE linked companies]
PSE – impact
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Training & education

Significant research advances

process design

process control
 process

operations

numerical methods & optimization

[software & other] tools
Beyond chemical engineering .. [?]
‘Traditional’ PSE
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PSE Core
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Mathematical Modelling
Process Synthesis
Product & Process Design
Process Operations
Process Control
Numerical Methods & Optimization
PSE evolution ..
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PSE Core
Recognition of length and time scales

From nano-scale (molecular)
to micro-scale (particles, crystals)
to meso-scale (materials, equipment, products)
to mega-scale (supply chain networks, environment)
PSE evolution ..
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PSE Core
Recognition of length and time scales

From nano-scale (molecular)
to micro-scale (particles, crystals)
to meso-scale (materials, equipment, products)
to mega-scale (supply chain networks, environment)
Multi-scale Modelling
Product Value Chain
Recognition of length and time scales
(Marquardt; Grossmann et al)
PSE evolution ...
Multi-scale
Modelling
PSE evolution ...
simulation
optimization
Multiscale
Modelling
synthesis
control
Product/process
design
PSE evolution

Recognition of length and time scales

From nano-scale (molecular)
to micro-scale (particles, crystals)
to meso-scale (materials, equipment, products)
to mega-scale (supply chain networks, environment)
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Core, generic enabling technology provider to other domains

molecular  genomic  biological  materials  energy 
automation  plants  oilfields  global supply chains
Multi-scale process systems engineering
Multi-scale Process Systems
Engineering
Molecular
Systems
Engineering
simulation
Biological
& Biomedical
Systems
Engineering
optimization
Multi-scale
Modelling
synthesis
control
Product/process
design
Supply Chain
Energy/Sustainability
Systems
Systems
Engineering
Engineering
Multi-scale PSE
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PSE Core
Domain-driven PSE
Problem-centric PSE
PSE Core
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Multi-scale Modelling
Multi-scale Optimization
Product & Process Design
Process Operations
Control & Automation
Domain-driven PSE
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Molecular Systems Engineering
 Materials Systems Engineering
 Biological Systems Engineering
 Energy Systems Engineering
Problem-centric PSE

Environmental systems engineering
 Safety systems engineering
 Manufacturing supply chains
Multi-scale Process Systems
Engineering
Molecular
Systems
Engineering
simulation
Biological
& Biomedical
Systems
Engineering
optimization
Multi-scale
Modelling
synthesis
control
design
Supply Chain
Systems
Engineering
Energy/Sustainability
Systems
Engineering
Multi-scale Process Systems
Engineering leads to ..
Molecular
Systems
Engineering
simulation
Biological
& Biomedical
Systems
Engineering
optimization
Multi-scale
Modelling
synthesis
control
design
Supply Chain
Systems
Engineering
Energy/Sustainability
Systems
Engineering
Model Based Innovation across the Process
ModelLifecycle
Operational
based
optimization
Process
flowsheeting
automation
Process
developmen
t
CONCEPT
Detailed
design of
complex
equipment
DESIGN
OPERATION
A
Optimization
of plant and
operating
procedures
Plant
TC
Troubleshooting/
Safety
Process Systems Engineering
.. provides the ‘scientific glue’ within
chemical engineering (Perkins, 2008)
Molecular
Driven
Chemical
Engineering
Bio-driven
Chemical
Engineering
Materials
Properties
Process
Systems
Transport Engineering Analytics/
Phenomena
Multi-scale
Chemical
Engineering
Experimental
Reaction
engineering
Energy -driven
Chemical
Engineering
Process Systems Engineering
‘systems thinking & practice’ – essential
to address societal grand challenges
Nano - materials
simulation
Health
optimization
Systems
Engineering
synthesis
Sustainable design
Manufacturing
control
Energy
Outline

A brief introduction

Chemical Engineering

Process Systems Engineering

On-going research areas & projects

Multi-parametric programming & control
Research Group research areas & current projects
Acknowledgements

Funding
 EPSRC - GR/T02560/01, EP/E047017, EP/E054285/1
 EU - MOBILE, OPTICO, PRISM, PROMATCH, DIAMANTE, HY2SEPS, IRSES
 CPSE Industrial Consortium, KAUST
 Air Products

People
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J. Acevedo, V. Dua, V. Sakizlis, P. Dua, N. Bozinis, P. Liu, N. Faisca, K.
Kouramas, C. Panos, L. Dominguez, A. Voelker, H. Khajuria, M. WittmannHohlbein, H. Chang
P. Rivotti, A. Krieger, R. Lambert, E. Pefani, M. Zavitsanou, E. Velliou, G.
Kopanos, A. Manthanwar, I. Nascu, M. Papathanasiou, N. Diangelakis, M.
Sun, R. Oberdieck
John Perkins, Manfred Morari, Frank Doyle, Berc Rustem, Michael
Georgiadis
Imperial & ParOS R&D Teams, Tsinghua BP Energy Centre
Current Research Focus Overview
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Multi-parametric programming & Model Predictive
Control [MPC]
Energy & Sustainability (driven) Systems
Engineering
Biomedical Systems Engineering
Energy and Sustainability (driven)
Systems
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Synthesis and Design
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Design of micro-CHP systems for residential applications
Design of poly-generation systems
Long-term design and planning of general energy systems under
uncertainty
Operations and control
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Scheduling under uncertainty of micro-CHP systems for residential
applications
Supply chain optimization of energy systems
Integration of design and control for energy systems – fuel cells,
CHPs
Integration of scheduling and control of energy systems under
uncertainty
Biomedical Systems Engineering
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Leukaemia – Development of optimal protocols for
chemotherapy drug delivery for:
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Experimental, modelling and optimization activity
Anaesthesia & Diabetes
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Acute Myeloid Leukaemia (AML)
Chronic Lymphocytic Leukaemia (CLL)
Emphasis on modelling and control in volatile anaesthesia
the artificial pancreas
Collaboration with Prof. Mantalaris and Dr. Panoskaltsis
Collaboration with Prof Frank Doyle, UC Santa-Barbara
Multi-Parametric
Programming & Explicit MPC
a progress report
Professor Stratos Pistikopoulos FREng
Outline
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Key concepts & historical overview
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Recent developments in multi-parametric
programming and mp-MPC
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MPC-on-a-chip applications
What is On-line Optimization?
MODEL/OPTIMIZER
Control
Actions
Data Measurements
SYSTEM
What is Multi-parametric Programming?

Given:



a performance criterion to minimize/maximize
a vector of constraints
a vector of parameters
z ( x)  min f (u , x)
u
s.t. g (u , x)  0
x Rn
u Rs
What is Multi-parametric Programming?

Given:




u
a performance criterion to minimize/maximize
s.t. g (u, x)  0
a vector of constraints
a vector of parameters
x Rn
Obtain:

z ( x)  min f (u, x)
u R
s
the performance criterion and the optimization
variables as a function of the parameters
 the regions in the space of parameters where these
functions remain valid
Multi-parametric programming
(1) Optimal look-up function
z ( x)  min f (u , x)
u
s.t. g (u , x)  0
x Rn
u R
(2) Critical Regions
s
u (x)
Obtain optimal solution u(x) as a
function of the parameters x
Multi-parametric programming
Problem Formulation
min
 3 u1  8 u2 
u1 ,u2
st.
1
5

 8

 4
1
 1


 4  u1  0





22  u 2   0


 1
0
0
  13  0





0  x1   20
0


 



 1 x 2   121 0


  
0
 8   0
 10  x1  10  100  x 2  100
Multi-parametric programming
Critical Regions
4 Feasible Region Fragments
100
CR001
CR002
CR003
CR004
80
60
40
x2x2
20
0
-20
-40
-60
-80
-100
-10
-8
-6
-4
-2
0
x1
x1
2
4
6
8
10
Multi-parametric programming
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U  
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Multi-parametric Solution
 1  0.0 3 1
  6.7 1 
 1

 5 
0

  x1 



0
1
0
 1



  0.3 3 0  x 1 
  1.6 7

 x 2 


 1.3 3 0  x    1 4.6 7  i f  0
1

  2




 100 



1
 0

 100 

 1  0.1 1 5 
8.6 5 
  1 0.0 3 1 
 6.7 1




  1 0.0 4 5   x 1 
 7.5 
 0.7 3  0.0 3  x 1 
5.5


   10 


i
f
x
1
0
0.2 6 0.0 3  x 
7.5

  2



  2


 0

 100 
1




1
 0

 100 
 0 0  x 1 
0


1 0 x 
1 3

  2
 
0 0.0 5  x 1 
1 1.8
0 0.0 6  x    9.8 

  2


 1  0.0 4 5 
  7.5
x


1
 1

0
  5 






if
x 2 
0
1
1
0
0








  1 0.1 1 
  8.6 5
x


if  1
0    1    10 

 x 2 


0

1
1
0
0








Multi-parametric programming
min 3u1  8u2 
u
st.
1
  13  0
 1 0 
1
  20  0
0 0 x
 5  4 u
 
   1  
   1  

  8 22  u2   0  1  x2   121 0
  





 8   0
0 0
  4  1
 10  x1  10,  100  x2  100
4 Feasible Region Fragments
100
CR001
CR002
CR003
CR004
80
60
40
x2
20
0
-20
-40
-60
-80
-100
-10
-8
-6
-4
-2
0
x1
2
4
6
8
10


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











U  















  0.3 3 3
 1.3 3 3

 0.7 3 3 3
0.2 6 6 6 7

0  x1 
  1.6 6 6 7

   1 4.6 6 6 7
0
  x2 


 0.0 3 3 3  x1 
5.5

  7.5
0.0 3 3 3 3
  x2 


if
if
0
1

0  x1 
0

  1 3
0
  x2 


if
0
0

0.0 5 1 2 8  x1 
1 1.8 4 6 2

  9.8 0 7 6 9
0.0 6 4 1 
  x2 


if
 1
 1

 1

 0

 0
 0.0 3 1 2 5

  6.7 1 8 7 5




0
5
  x1 



0
1
0




  x2 


1
1
0
0






1
100



 1
 1

 1

 1
 0

 0
 0.1 1 5 3 8 5

8.6 5 3 8 5
6.7 1 8 7 5

0.0 3 1 2 5 



0.0 4 5 4 5 4 5
7.5
  x1 


 



x
0
1
0
2







1
100 



1

 100 
 1
 1

 0

 0.0 4 5 4 5 4 5

  7.5
   x1    5 
0

 


x
1
  2



 100 
 1
 1

 0

0.1 1 5 3 8 5

  8.6 5 3 8 5

   x1   

0
10

 


x
1
100
  2





Only 4 optimization problems solved!
On-line Optimization via off-line
Optimization
POP
PARAMETRIC PROFILE
OPTIMIZER
Control
Actions
System
State
SYSTEM
Control
Actions
System
State
SYSTEM
Function Evaluation!
Multi-parametric/Explicit Model
Predictive Control

Compute the optimal sequence of manipulated inputs which minimizes
tracking error = output – reference
subject to constraints on inputs and outputs

On-line re-planning: Receding Horizon Control
Multi-parametric/Explicit Model
Predictive Control

Compute the optimal sequence of manipulated inputs which minimizes
Solve a QP at each time interval

On-line re-planning: Receding Horizon Control
Multi-parametric Programming Approach
State variables  Parameters
 Control variables  Optimization variables

MPC  Multi-Parametric Programming
problem
 Control variables  F(State variables)

Multi-parametric Quadratic Program
Explicit Control Law
2
CR0
CR1
CR2
1.5
J ( x(t ))  min   xTt  j |t x t  j |t  0.01 ut2 j |t  xTt 2 |t P x t 2 |t
1
ut |t , ut 1|t
1
j 0
 0.7326  0.0861
0.0609 
s.t x t  j 1|t  
 x t  j |t  0.0064  ut  j |t
0
.
1722
0
.
9909




 2  ut  j |t  2 j  1,2 x t |t  x(t )
0.5
x2
0
-0.5
-1
-1.5
-2
-2
-1.5
-1
-0.5
0
x1

 0.7059 0.7083 
0.2065







6
.
8355

6
.
8585
x
t
i
f
x
t


 0.7059  0.7083 
0.2065






ut   
2
i f  0.7059  0.7083  x t    0.2065



2
i f 0.7059 0.7083  x t    0.2065

0.5
1
1.5
2
Multi-parametric Controllers
(1) Optimal look-up function
Optimization Model
(2) Critical Regions
Parametric Controller
Measurements
Control Action
SYSTEM
System Outputs
Input Disturbances
 Explicit Control Law
MPC-on-a-chip!
 Eliminate expensive, on-line computations
 Valuable insights !
A framework for multi-parametric
programming & MPC (Pistikopoulos 2008, 2009)
Modelling/
Simulation
Identification/
Approximation
‘High-Fidelity’
Dynamic Model
System Identification
Model Reduction
Techniques
‘Approximate Model’
Model-Based Control
& Validation
Multi-Parametric
Programming (POP)
Extraction of
Parametric Controllers
u = u ( x (θ ) )
Closed-Loop
Control System Validation
A framework for multi-parametric
programming and MPC (Pistikopoulos 2010)
On-line Embedded
Control:
Off-line Robust
Explicit Control
Design:
Modelling/
Simulation
Identification/
Approximation
EMBEDDED
CONTROLLER
REAL SYSTEM
‘High-Fidelity’
Dynamic Model
Model Reduction
Techniques
System
Identification
‘Approximate
Model’
Model-Based
Control &
Validation
Multi-Parametric
Programming
(POP)
Extraction of
Parametric
Controllers
u = u ( x(θ) )
Closed-Loop
Control System
Validation
Key milestones-Historical Overview
AIChE J.,Perspective (2009)


Number of publications
Multi-Parametric
Programming
Multi-Parametric
MPC &
applications
Pre-1999
>100
0
Post-1999
~70
250+
2002 Automatica paper - citations [Sep 2014]: 900+
WoS; 1200+ Scopus; 1650+ Google Scholar

Multi-parametric programming – until 1992 mostly
analysis & linear models

Multi-parametric/explicit MPC – post-2002 much
wider attention
Multi-parametric Programming
Theory
mp-LP
Gass & Saaty [1954], Gal & Nedoma [1972], Propoi [1975], Adler and Monterio [1992], Gal
[1995], Acevedo and Pistikopoulos[1997], Dua et al [2002], Pistikopoulos et al [2007]
mp-QP
Townsley [1972], Propoi [1978], Best [1995], Dua et al [2002], Pistikopoulos et al [2002,2007]
mp-NLP
Fiacco [1976],Kojima [1979], Bank et al [1983], Fiacco [1983], Fiacco & Kyoarisis [1986],
Acevedo & Pistikopoulos [1996], Dua and Pistikopoulos [1998], Pistikopoulos et al [2007]
mp-DO
Sakizlis et al.[2002], Bansal [2003], Sakizlis et al [2005], Pistikopoulos et al [2007]
mp-GO
Fiacco [1990], Dua et al [1999,2004], Pistikopoulos et al [2007]
mp-MILP
Marsten & Morin [1975], Geoffrion & Nauss [1977], Joseph [1995], Acevedo & Pistikopoulos
[1997,1999], Dua & Pistikopoulos[ 2000]
mp-MINLP
McBride & Yorkmark [1980], Chern [1991], Dua & Pistikopoulos [1999], Hene et al [2002], Dua et
al [2002]
Multi-parametric/Explicit Model
Predictive Control Theory
mp-MPC
Pistikopoulos [1997, 2000], Bemporad, Morari, Dua & Pistikopoulos [2000], Sakizlis &
Pistikopoulos [ 2001], Tondel et al [2001], Pistikopoulos et al [2002], Bemporad et al [2002],
Johansen and Grancharova [2003], Sakizlis et al [2003], Pistikopoulos et al [2007]
mp-Continuous
MPC
Sakizlis et al [2002], Kojima & Morari[ 2004], Sakizlis et al [2005], Pistikopoulos et al [2007]
Hybrid mp-MPC
Bemporad et al [2000], Sakizlis & Pistikopoulos [2001], Pistikopoulos et al [2007]
Robust mpMPC
Kakalis & Pistikopoulos [2001], Bemporad et al [2001], Sakizlis et al [2002], Sakizlis &
Pistikopoulos [2002], Sakizlis et al [2004], Olaru et al [2005], Faisca et al [2008]
mp-DP
Nunoz de la Pena et al [2004],Pistikopoulos et al [2007],Faisca et al [2008]
mp-NMPC
Johansen [2002], Bemporad [2003], Sakizlis et al [2007], Dobre et al [2007], Narciso &
Pistikopoulos [2009]
68
Patented Technology

Improved Process Control
European Patent No EP1399784, 2004

Process Control Using Co-ordinate Space
United States Patent No US7433743, 2008
Multi-parametric programming & Model
Predictive Control [MPC]

Theory of multi-parametric programming




Multi-parametric mixed integer quadratic programming [mp-MIQP]
Multi-parametric dynamic optimization [continuous-time, mp-DO]
Multi-parametric global optimization
Theory of multi-parametric/explicit model predictive control
[mp-MPC]




Explicit robust MPC of hybrid systems
Explicit MPC of continuous time-varying [dynamic] systems
Explicit MPC of periodic systems
Moving Horizon Estimation & mp-MPC
Multi-parametric programming & Model
Predictive Control [MPC] – cont’d

Framework for multi-parametric programming & control



Model approximation [from high fidelity models to the design of
explicit MPC controllers]
Software development, prototype & demonstrations [for teaching &
research]
Application areas






Fuel cell energy system – experimental/laboratory
Car system control – prototypes/laboratory
Energy systems [CHP and micro-CHP]
Bio-processing [continuous production & control of monoclonal
antibodies]
Pressure Swing Absorption [PSA] and hybrid systems
Biomedical Systems
MPC-on-a-chip Applications – Recent
Developments

Process Control
 Air
Separation (Air Products)
 Hybrid PSA/Membrane Hydrogen Separation
(EU/HY2SEPS, KAUST)

Automotive
 Active

Valve Train Control (Lotus Engineering)
Energy Systems
 Hydrogen
 Fuel
Cell
Storage (EU/DIAMANTE)
MPC-on-a-chip Applications – Recent
Developments

Biomedical Systems (MOBILE - ERC Advanced Grant
Award)
 Drug/Insulin,
Anaesthesia and
Chemotherapeutic Agents Delivery Systems

Imperial Racing Green
 Fuel

cell powered Student Formula Car
Aeronautics (EPSRC)
 (Multiple)
Unmanned Air Vehicles – with
Cranfield University
Small Air Separation Units
(Air Products, Mandler et al,2006)

Enable advanced MPC for small
separation units
Optimize performance
Minimize operating costs
Satisfy product and equipment
constraints

Parametric MPC ideally suited


Supervises existing regulatory control
Off-line solution with minimum on-line
load
 Runs on existing PLC
 Rapid installation compared to traditional
MPC

Advantages of Parametric MPC




5% increased throughput
5% less energy usage
90% less waste
Installation on PLC in 1-day
Active Valve Train Control
(Lotus Engineering, Kosmidis et al, 2006)

Active Valve Trains (AVT):


Optimum combustion efficiency, Reduced
Emissions, Elimination of butterfly valve,
Cylinder deactivation, Controlled auto-ignition
(CAI), Quieter operation
Basic idea:

Control System sends signal to valve
 This actuates piston attached to engine
valve
 Enables optimal control of valve timing
over entire engine rpm range
Challenges for the AVT control



Nonlinear system dynamics: Saturation,
flow non-linearity, variation in fluid
properties, non-linear opening of the orifices
Robustness to various valve lift profiles
Fast dynamics and sampling times (0.1ms)
Multi-parametric Control of H2 Storage
in Metal-Hydride Beds (EU-DIAMANTE, Georgiadis
et al, 2008)



Tracking the optimal temperature profile
Ensure economic storage – expressed by
the total required storage time
Satisfy temperature and pressure
constraints
1.12
Tf(z=1) with controller
Tf(z=1) without controller
1.1
Optimal look-up table
(Projected on the yt - ut plane)
Tf(z=1)
1.08
1.06
1.04
1.02
1
0
100
200
300
400
time
500
600
700
800
PEM Fuel Cell Unit
PI
MassFlow
H2
PI
N2
TE
MassFlow
Electronic
Load
PT
TE
VENT
Hydrator
PI
TE
PT
TE
PT
A
VENT
PDT
K
Air
MassFlow
TE
TE
Water
PT
TE
H2O
PT
TE
M
Hydrator
Filter
Radiator
Collaborative work with Process Systems Design &
Implementation Lab (PSDI) at CERTH - Greece
PEM Fuel Cell Unit
Unit Specifications
 Fuel Cell : 1.2kW
 Anode Flow : 5..10 lt/min
 Cathode Flow : 8..16 lt/min
 Operating Temperature : 65 – 75 °C
 Ambient Pressure
PEM Fuel
Cell System
mH2
mAir
mcool
TYHydrators
Vfan
Control Strategy
Start-up Operation
Heat-up Stage : Control of coolant loop
Nominal Operation
Control Variables :
 Mass Flow Rate of Hydrogen & Air
 Humidity via Hydrators temperature
 Cooling system via pump regulation
 Known Disturbance : Current
Unit Design : Centre For Research & Technology Hellas (CERTH)
Tst
HTst
(1) Optimal look-up
function
(2) Critical Regions
79
80
81
82
Imperial Racing Green Car

Student Formula Project

Control of Start-up/Shutdown of the FC
Traction Motion Control

FPGA
(MPC-on-a-Chip)
Control & Acquisition
System
Biomedical Systems (MOBILE ERC Advanced Grant)
Step 1: The sensor measures
the glucose concentration from
the patient
Step 2: The sensor then inputs
the data to the controller which
analyses it and implements the
algorithm
2
Sensor
Controller
1
Patient
Insulin Pump
3
Step 3: After analyzing the
data the controller then signals
the pump to carry out the
required action
4
Step 4: The Insulin Pump
delivers the required dose to
the patient intravenously
University Politehnica of
Bucharest Doctor Honoris Causa
Mulțumesc!
University Politehnica of
Bucharest Doctor Honoris Causa
Professor Stratos Pistikopoulos FREng

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