Identification for Automotive Systems

356 Pages·2012·11.251 MB·English

by  Daniel Alberer, Håkan Hjalmarsson, Luigi lRe (eds.)

Lecture Notes
in Control and Information Sciences 418
Editors:M.Thoma,F.Allgöwer,M.Morari
Daniel Alberer, Håkan Hjalmarsson,
and Luigi del Re (Eds.)
Identification for Automotive
Systems
ABC
SeriesAdvisoryBoard
P.Fleming,P.Kokotovic,
A.B.Kurzhanski,H.Kwakernaak,
A.Rantzer,J.N.Tsitsiklis
Editors
Dr.DanielAlberer Prof.LuigidelRe
InstituteforDesignandControlof InstituteforDesignandControlof
MechatronicalSystems, MechatronicalSystems,
JohannesKeplerUniversityLinz JohannesKeplerUniversityLinz
Altenbergerstr.69 Altenbergerstr.69
4040Linz 4040Linz
Austria Austria
E-mail:[email protected] E-mail:[email protected]
Prof.HåkanHjalmarsson
SchoolofElectricalEngineering
AutomaticControl
KTHRoyalInstituteofTechnology
Osquldasv.10
SE-10044Stockholm
E-mail:[email protected]
ISBN978-1-4471-2220-3 e-ISBN978-1-4471-2221-0
DOI10.1007/978-1-4471-2221-0
LectureNotesinControlandInformationSciences ISSN0170-8643
LibraryofCongressControlNumber:2011938110
(cid:2)c 2012Springer-VerlagLondonLimited
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Preface
The increasing complexity of automotive systems and the high number of
variants make the exploitationof available degrees of freedom more difficult.
Model based control is generally advocated as a solution or at least as a
supportinthisdirection,butmodelingisnotasimpleissue,atleastforsome
problemsinautomotiveapplications,e.g.inemissionsbasedcontrol.Classical
first principle methods are extremely helpful because they provide a good
insight into the operation of the systems, but frequently require too much
effortand/ordonotachievetherequiredprecisionand/orarenotsuitablefor
onlineuse.Thereismuchknow-howavailableintheidentificationcommunity
to improve this, either by purely parameter estimation based approaches or
by mixed models, but this know-how is frequently not tailored for the needs
of the automotive community, and the cooperation is actually quite limited.
Against this background, a workshop organized by the Austrian Center
of Competence in Mechatronics (ACCM) took place at the Johannes Ke-
plerUniversityLinz(Austria)fromJuly15-16,2010,whichbroughttogether
users, in particular from the industry, identification experts, in particular
from the academy, and experts in both worlds. The contents of this book
are peer reviewed versions of the workshop contributions and are structured
into four parts, starting with an assessmentof the need for (nonlinear) iden-
tification methods, followed by a presentation of suitable methods, then a
discussionon the importance of data is addressedand finally the description
of several applications of identification methods for automotive systems is
presented.
Neither the workshopnor this collectionof contributionswouldhavebeen
possiblewithoutthesupportofseveralpeople(inparticularofDanielaHum-
merandMichaelaBeneder).Thanksareduealsotothereviewersofthesingle
chapters who have done an important and essential work.
Organization
Steering Organization
Austrian Center of Competence in Mechatronics, Linz, Austria
Hosting Organization
Johannes Kepler University Linz, Austria
Program Committee
Daniel Alberer Johannes Kepler University Linz, Austria
Rolf Johansson Lund University, Sweden
Ilya Kolmanovsky University of Michigan, USA
Sergio Savaresi Politecnico di Milano, Italy
Greg Stewart Honeywell, Canada
Organizing Committee
Daniel Alberer Johannes Kepler University Linz, Austria
Daniela Hummer Johannes Kepler University Linz, Austria
Referees
J.C. Agu¨ero X.J.A. Bombois M. Deistler
R. Backman A. Brown P. Dickinson
O. Ba¨nfer D. Cieslar M. Enqvist
W. Baumann M. Corno L. Eriksson
C. Benatzky A. Darlington D. Filev
VIII Organization
S. Formentin S. Jakubek D. Pachner
M.A. Franchek H. Jammoussi T. Polo´ni
D. Germann R. Johansson K. Ro¨pke
C. Guardiola J.P. Jones P. Tunest˚al
M. Hirsch I. Kolmanovsky C. Vermillion
H. Hjalmarsson A. Marconato C.P. Vyasarayani
E. Ho¨ckerdal T. McKelvey A. Wills
B. Houska A. Ohata S. Winkler
Contents
1 System Identification for Automotive Systems:
Opportunities and Challenges ........................... 1
Daniel Alberer, H˚akan Hjalmarsson, and Luigi del Re
1.1 Introduction......................................... 1
1.2 Methods for System Identification of
Automotive Systems.................................. 5
1.3 Applications......................................... 7
1.4 Conclusions and Outlook.............................. 8
References ................................................ 9
Part I: Needs and Chances of Nonlinear Identification for
Automotive Systems
2 A Desired Modeling Environment for Automotive
Powertrain Controls ..................................... 13
Akira Ohata
2.1 Introduction......................................... 13
2.2 Proposed Modeling Environment ....................... 16
2.3 Physical Modeling Environment........................ 17
2.4 Empirical Modeling Environment....................... 22
2.5 Integration of Physical and Empirical Models ............ 23
2.5.1 Approach A.................................. 24
2.5.2 Approach B .................................. 28
2.6 Summary ........................................... 32
References ................................................ 33
3 An Overview on System-Identification Problems in
Vehicle Chassis Control.................................. 35
Simone Formentin and Sergio M. Savaresi
3.1 Introduction......................................... 35
3.2 Example 1: Grey-Box Identification for Yaw Control of
Four-Wheeled Vehicles ([8])............................ 36
X Contents
3.3 Example 2: Black-Box Identification of Engine-to-Slip
Dynamics for Motorcycle Traction Control ([9]) .......... 39
3.4 Example 3: Estimation-OrientedIdentification for Sensor
Reduction in Semi-active Suspension Systems
for Cars ([10]) ....................................... 42
3.5 Example 4: Direct Braking Control Design for
Two-Wheeled Vehicles ([11])........................... 43
3.6 Conclusions ......................................... 47
References ................................................ 48
Part II: Suitable Identification Methods
4 Linear Parameter-Varying System Identification: The
Subspace Approach...................................... 53
M. Corno, J.-W. van Wingerden, and M. Verhaegen
4.1 Introduction......................................... 53
4.2 LPV System Identification Overview.................... 54
4.3 LPV Subspace Identification........................... 55
4.4 Simulation Example .................................. 60
4.4.1 Analytical LPV Modeling ...................... 60
4.4.2 Simulation Results ............................ 61
4.5 Conclusions ......................................... 63
References ................................................ 64
5 A Tutorial on Numerical Methods for State and
Parameter Estimation in Nonlinear Dynamic Systems ... 67
Boris Houska, Filip Logist, Moritz Diehl, and Jan Van Impe
5.1 Introduction......................................... 68
5.1.1 What Are the Different Classes of
Dynamic Models?............................. 68
5.1.2 How to Calibrate Models? The Modeling Cycle ... 68
5.1.3 Approaches for the Optimization of Dynamic
Systems ..................................... 69
5.2 Maximum Likelihood Estimation for Differential
Equation Models ..................................... 71
5.2.1 Interpretation of Given Prior Information ........ 72
5.2.2 Smoothing Heuristics.......................... 72
5.2.3 The Importance of Convexity................... 73
5.2.4 Estimation of Time-Varying Parameters ......... 74
5.2.5 Least Squares Terms versus l1-Norms............ 74
5.3 Generalized Gauss-Newton Methods .................... 75
5.4 Schlo¨der’s Trick or the Lifted Newton Type Method for
Parameter Estimation ................................ 77
5.4.1 Modular ForwardLifting Techniques ............ 78
5.4.2 Automatic Backward Lifting Techniques ......... 80
Contents XI
5.5 State and Parameter Estimation with ACADO Toolkit.... 82
5.6 Conclusions ......................................... 84
References ................................................ 85
6 Using Genetic Programming in Nonlinear Model
Identification ............................................ 89
Stephan Winkler, Michael Affenzeller, Stefan Wagner,
Gabriel Kronberger,and Michael Kommenda
6.1 Evolutionary Computation and Genetic Algorithms....... 89
6.2 Genetic Programmingand Its Use in
System Identification ................................. 91
6.2.1 Basics of Genetic Programming................. 91
6.2.2 Evolutionary Structure Identification Using
Genetic Programming ......................... 92
6.3 Application Examples................................. 94
6.3.1 Designing Virtual Sensors for Emissions (NO ,
x
Soot) of Motor Engines ........................ 94
6.3.2 Quality Pre-assessment in Steel Industry Using
Data Based Estimators ........................ 95
6.3.3 Medical Data Analysis......................... 96
6.4 Algorithmic Enhancements ............................ 96
6.4.1 Enhanced Selection Concepts................... 96
6.4.2 On-Line and Sliding Window Genetic
Programming................................. 98
6.4.3 Cooperative Evolutionary Data Mining Agents ... 99
6.5 Algorithm Analysis: Population Diversity Dynamics ...... 101
Acknowledgements ......................................... 102
References ................................................ 103
Appendix: The HeuristicLab Framework for Heuristic
Optimization ........................................ 106
7 Markov Chain Modeling and On-Board Identification
for Automotive Vehicles ................................. 111
Dimitar P. Filev and Ilya Kolmanovsky
7.1 Introduction......................................... 111
7.2 Generalization of Conventional Markov Chain through
Interval and Fuzzy Granulation ........................ 113
7.2.1 Markov Chains with Interval Encoding.......... 114
7.2.2 Markov Chains with Fuzzy Encoding ........... 115
7.3 General Algorithm for On-Line Learning of the
Transition Probability Matrix ......................... 119
7.4 Markov Chain Models of Vector-Valued Signals.......... 123
7.5 Conclusions ......................................... 126
References ................................................ 127