New book on neurcontrol

[rafal@mech.gla.ac.uk]

Contributed by:

   Rafal Zbikowski, PhD
        Control Group, Department of Mechanical Engineering, 
        Glasgow University, Glasgow G12 8QQ, Scotland, UK
   rafal at mech.gla.ac.uk



			NEW BOOK ON NEUROCONTROL

		``Neural Adaptive Control Technology''
		  R Zbikowski and K J Hunt (Editors)
		        World Scientific, 1996
		          ISBN 981-02-2557-1 
		   hard bound, 340pp, subject index

Full details:
	http://www.mech.gla.ac.uk/~nactftp/nact.html
	http://www.singnet.com.sg/~wspclib/Books/compsci/3021.html 


				Summary
				^^^^^^^
This book is an outgrowth of the workshop on Neural Adaptive Control
Technology, NACT I, held in 1995 in Glasgow.  Selected workshop
participants were asked to substantially expand and revise their
contributions to make them into full papers.
 
The workshop was organised in connection with a three-year European
Union funded Basic Research Project in the ESPRIT framework, called
NACT,  a collaboration between Daimler-Benz (Germany) and the
University of Glasgow (Scotland).  A major aim of the NACT project is
to develop a systematic engineering procedure for designing neural
controllers for non-linear dynamic systems. The techniques developed
are being evaluated on concrete industrial problems from Daimler-Benz.
 
In the book emphasis is put on development of sound theory of neural
adaptive control for non-linear control systems, but firmly anchored
in the engineering context of industrial practice. Therefore the
contributors are both renowned academics and practitioners from major
industrial users of neurocontrol.


				Contents
				^^^^^^^^

Part I Neural Adaptive Control Technology

Chapter 1
J. C. Kalkkuhl and K. J. Hunt (Daimler-Benz AG)
``Discrete-time Neural Model Structures for Continuous Nonlinear
  Systems:  Fundamental Properties and Control Aspects''

Chapter 2
P.~J.~Gawthrop (University of Glasgow)
``Continuous-Time Local Model Networks''

Chapter 3
R. {\.Z}bikowski and A. Dzieli{\'n}ski (University of Glasgow)
``Nonuniform Sampling Approach to Control Systems Modelling with
  Feedforward Neural Networks''

Part II Non-linear Control Fundamentals for Neural Networks

Chapter 4
W. Respondek 
(Polish Academy of Sciences)
``Geometric Methods in Nonlinear Control Theory: A Survey''

Chapter 5
T. Kaczorek (Warsaw University of Technology)
``Local Reachability, Local Controllability and Observability of a
  Class of 2-D Bilinear Systems''

Chapter 6
T. A. Johansen and M. M. Polycarpou
(SINTEF and University of Cincinnati)
``Stable Adaptive Control of a General Class of Non-linear Systems''

Part III Neural Techniques and Applications

Chapter 7
J-M. Renders and M. Saerens (Universit{\'e} Libre de Bruxelles)
``Robust Adaptive Neurocontrol of MIMO Continuous-time Processes
  Based on the $e_1$-modification Scheme''

Chapter 8
I. Rivals and L. Personnaz
({\'E}cole Superieure de Physique et de Chimie Industrielles)
``Black-Box Modeling with State-Space Neural Networks''

Chapter 9
D. A. Sofge and D. L. Elliott (NeuroDyne, Inc.)
``An Approach to Intelligent Identification and Control of Nonlinear
  Dynamical Systems''

Chapter 10
W. S. Mischo (Darmstadt Institute of Technology)
``How to Adapt in Neurocontrol: A Decision for CMAC''

Chapter 11
G. T. Lines and T. Kavli (SINTEF Instrumentation)
``The Equivalence of Spline Models and Logic Applied to Model
  Construction and Interpretation''

Index


				Preface
				^^^^^^^
This book is an outgrowth of the workshop on Neural Adaptive Control
Technology, NACT I, held on May 18--19, 1995 in Glasgow.  However,
this book is not simply the conference proceedings. Instead, selected
workshop participants were asked to substantially expand and revise
their contributions to make them into full papers.

Before the contents of the book is discussed, it seems in order to
briefly sketch the background and purpose of the workshop. The event
was organised in connection with a three-year European Union funded
Basic Research Project in the ESPRIT framework, called NACT,  a
collaboration between Daimler-Benz (Germany) and the University of
Glasgow (Scotland).

The NACT project, which began on 1 April 1994, is a study of the
fundamental properties of neural network based adaptive control
systems. Where possible, links with traditional adaptive control
systems are exploited. A major aim is to develop a systematic
engineering procedure for designing neural controllers for non-linear
dynamic systems. The techniques developed are being evaluated on
concrete industrial problems from within the Daimler-Benz group of
companies.

This context dictated the focus of the workshop and guided the
editors in the choice of the papers and their subsequent reshaping
into substantive book chapters. Thus, emphasis is put on development of
a sound theory of neural adaptive control for non-linear control
systems, but firmly anchored in the engineering context of industrial
practice. Therefore, the contributors are both renowned academics and
practitioners from major industrial users of neurocontrol.

The book naturally divides into three parts.

Part I is devoted to the theoretical and practical results on
neural adaptive control technology resulting from the NACT project.
Chapter 1 by J.~C.~Kalkkuhl and K.~J.~Hunt analyses several important
fundamental issues so far largely ignored in the neurocontrol
context. The issue of prime importance, and thus
treated first, is that of the discretisation of continuous-time
models. The physical plants are continuous-time, but the practicality
of digital implementations require discrete-time representations. A
careful discussion is presented exposing the limitations of NARMAX
models, widely used in neurocontrol.  This sets the stage for the
finite-element method approach to approximation of NARMAX models.
Chapter~2 is written by Peter J.~Gawthrop, a well-known contributor to
adaptive control, in particular, continuous-time self-tuning.
Following this line, the continuous-time version of Local Model
Networks/Controllers is introduced. The structure has some surprising
connections to the state observation problem. This leads to the
important distinction between local and global states of the model.
The exposition is accompanied by simulations of essentially
non-linear systems.  Chapter 3 by R.~{\.Z}bikowski and
A.~Dzieli{\'n}ski introduces and describes in some detail the
nonuniform multi-dimensional sampling approach to neurocontrol with
feedforward neural networks. The authors argue that this is a natural
theoretical framework for practical control engineering problems,
because the measured data representing the NARMA model come as
multidimensional samples. The dynamics of the underlying system
manifest themselves by nonuniformity of the data and thus the
irregular spread of the samples is an essential feature of the
representation. A novel method of neural modelling of NARMA systems
is given. Important practical issues of distortions caused by
approximation of the Fourier transform are addressed. A tutorial
survey of the theory of Paley-Wiener functions, with emphasis on the
neural modelling aspects, completes the presentation.

Part II is devoted to results of non-linear control, relevant
to theory of neurocontrol.  It opens with Chapter 4 by Witold
Respondek, whose pioneering work in the beginning of the 1980s
resulted in explosive development (lasting to this day) of geometric
methods in non-linear control. In fact, `geometric control' has
practically become synonymous with non-linear control. Recently, the
rich and consistent theory has been used more often in the context of
neurocontrol, because of the need for a control framework for
(non-linear) neural models.  Being an important and active
participant in the development of the geometric approach, Respondek
offers valuable insights into the underlying mathematics, while never
compromising on rigour. His lucid style is supported by numerous
illustrations and examples making the chapter a readable and
informative introduction. It is a welcome feature for a subject
dominated by presentations often deprived of geometric feeling and
overloaded with distracting technicalities. Chapter 5 gives another
theoretical perspective, this time from Tadeusz Kaczorek, a major
contributor to the theory of 2-D control systems.  Chapter 6 by
T.~A.~Johansen and M.~M.~Polycarpou deals with the important, yet
often neglected, issue of stability of adaptive control of non-linear
systems.

Part III presents various aspects of neural control and its
applications. It starts with Chapter 7 by J-M.~Renders and
M.~Saerens. These authors develop local stability results for an
adaptive neurocontrol strategy. Weight adaptation is based upon
Lyapunov stability theory.  The next chapter, Chapter 8, is
co-authored by the well-known neural networks researchers I.~Rivals
and L.~Personnaz who consider state-space models for neurocontrol as
an alternative to the predominant input-output approach.  Chapter 9
brings the intelligent control perspective on neural issues by one of
the major players in the field, Donald A.~Sofge. Chapter 10 by
W.~S.~Mischo (from Henning Tolle's CMAC school) presents theory and
applications of CMAC-type memories for learning control. Finally,
Chapter 11 by G.~Lines and T.~Kavli presents the results of applying
spline-based adaptive methods for the development of dynamics models.
The interpretation of the spline models as fuzzy systems is also
examined.

		Rafa{\l} \.Zbikowski, Kenneth Hunt
		Glasgow, Berlin: December, 1995