Description
Book SynopsisThis 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 nonlinear 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 nonlinear 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.
Table of ContentsPart 1 Neural adaptive control technology: discrete-time neural model structures for continuous nonlinear systems - fundamental properties and control aspects, J.C. Kalkkuhl and K.J. Hunt; continuous-time local model networks, P.J. Gawthrop; nonuniform sampling approach to control systems modelling with feedforward networks, R. Zbikowski and A. Dzielinski. Part 2 Nonlinear control fundamentals for neural networks: geometric methods in nonlinear control theory - a survey, W. Respondek; local reachability and local controllability of a class of 2-D bilinear systems, T. Kaczorek; stable adaptive control of a general class of nonlinear systems, T.A. Johansen and M.M. Polycarpou. Part 3 Neural techniques and applications: process modelling with state-space neural predictors, I. Rivals et al; an approach to intelligent identification and control on nonlinear dynamic systems, applied to optimizing lean-burn engine controls, D.A. Sofge and D.L. Elliott; improved CMAC-type memories for learning control, W.S. Mischo; interpretation and analysis of empirical models identified with the ASMOD algorithm, T. Kavli and G. Lines.
