New Book: Multiple Model Approaches to Nonlinear Modelling and Control

[R. Murray-Smith]

New Book. Full details available at

http://www.itk.ntnu.no/SINTEF/ansatte/Johansen_Tor.Arne/mmamc/mmamc_book.html


             Multiple Model Approaches to Modelling and Control

             Roderick Murray-Smith and Tor Arne Johansen (Eds.)

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This book presents a variety of approaches which produce complex
models or controllers by piecing together a number of simpler
subsystems. This divide-and-conquer strategy is a long-standing and
general way of coping with complexity in engineering systems, nature and
human problem solving.

More complex plants, advances in information technology, and tightened
economical and environmental constraints in recent years have lead to
practising engineers being faced with modelling and control problems
of increasing complexity. When confronted with such problems, there is
a strong  intuitive appeal in building systems which operate robustly
over a wide range of operating conditions by decomposing them into a
number of simpler linear modelling or control problems, even for
nonlinear modelling or control problems. This appeal has been a factor
in the development of increasingly popular `local' and multiple-model
approaches to coping with strongly nonlinear and time-varying systems.

Such local approaches are directly based on the divide-and-conquer
strategy, in the sense that the core of the representation of the
model or controller is a partitioning of the system's full range of
operation into multiple smaller operating regimes each of which is
associated a locally valid model or controller. This can often give a
simplified and transparent nonlinear model or control
representation. In addition, the local approach has computational
advantages, it lends itself to adaptation and learning algorithms, and
allows direct incorporation of high-level and qualitative plant
knowledge into the model. These advantages have proven to be very
appealing for industrial applications, and the practical, intuitively
appealing nature of the framework is demonstrated in chapters
describing applications of local methods to problems in the process
industries, biomedical applications and autonomous systems. The
successful application of the ideas to demanding problems is already
encouraging, but creative development of the basic framework is needed
to better allow the integration of human knowledge with automated
learning.

The underlying question is `How should we partition the system - what
is `local'?'. This book presents alternative ways of bringing
submodels together, which lead to varying levels of performance and
insight. Some are further developed for autonomous learning of
parameters from data, while others have focused on the ease with which
prior knowledge can be incorporated. It is interesting to note that
researchers in Control Theory, Neural Networks, Statistics, Artificial
Intelligence and Fuzzy Logic have more or less independently developed
very similar modelling methods, calling them Local Model Networks,
Operating Regime based Models, Multiple Model Estimation and Adaptive
Control, Gain Scheduled Controllers Heterogeneous Control, Mixtures of
Experts, Piecewise Models, Local Regression techniques, or
Tagaki-Sugeno Fuzzy Models, among other names. Each of these
approaches has different merits, varying in the ease of introduction
of existing knowledge, as well as the ease of model
interpretation. This book attempts to outline much of the common
ground between the various approaches, encouraging the transfer of
ideas.

Recent progress in algorithms and analysis is presented, with
constructive algorithms for automated model development and control
design, as well as techniques for stability analysis, model
interpretation and model validation.

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			Table Of Contents

Preface - the book outline.

The Operating Regime Approach to Nonlinear Modelling and Control
	Tor Arne Johansen, SINTEF, and 
	Roderick Murray-Smith, Daimler-Benz AG

Fuzzy Set Methods for Local Modelling and Identification
	R. Babuska and H.B. Verbruggen, Delft University of Technology

Modelling of Electrically Stimulated Muscle 
	H. Gollee, University of Glasgow, K.J. Hunt, Daimler-Benz AG, 
	N. Donaldson, University College London and 
	J. Jarvis, University of Liverpool

Process Modelling Using the Functional State Approach
	Aarne Halme, Arto Visala and 
	Xia-Chang Zhang, Helsinki University of Technology

Markov Mixtures of Experts
	Marina Meila, 
	Michael Jordan, Massachusetts Institute of Technology

Active Learning with Mixture Models
	David Cohn, and Zoubin Ghahramani and Michael Jordan, 
	Massachusetts Institute of Technology

Local Learning in Local Model Networks
	Roderick Murray-Smith, Daimler-Benz AG and 
	Tor Arne Johansen, SINTEF

Side-Effects of Normalising Basis Functions in Local Model Networks
	Robert Shorten and Roderick Murray-Smith, Daimler-Benz AG

The Composition and Validation of Hetrogeneous Control Laws
	B. Kuipers, University of Texas at Austin and 
	K. Astrom, Lund Insitute of Technology

Local Laguerre Models
	Daniel Sbarbaro, University of Concepcisn

Multiple Model Adaptive Control
	Kevin D. Schott, 
	B. Wayne Bequette, Rensselaer Polytechnic Institute

H-infinity Control of Nonlinear Processes Using Multiple Linear Models
	A. Banerjee, Y. Arkun, Georgia Insitute of Technology, and 
	R. Pearson and B. Ogunnaike, DuPont

Synthesis of Fuzzy Control Systems Based on Linear Takagi-Sugeno 
Fuzzy Models
	J. Zhao, R. Gorez and 
	V. Wertz, Catholic University of Louvain

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