Warwick Kevin (ed.). Implementation of Self-tuning Controllers

London: Peter Peregrinus Ltd, 1988. — 364 p. — (IEE Control Engineering Series 35). — ISBN: 0-86341-127-4.This text is an extremely useful guide for those wishing to investigate the application of self-tuning control systems. The contents have been chosen in order to restrict the amount of theoretical detail to that necessary for explanation purposes, whilst application examples and programming suggestions are highlighted. The overall text is suitable for those wishing to gain the flavour of adaptive control, although those already familiar with self-tuning techniques will find the problem solutions discussed to be most attractive. Parameter estimation, numerical solutions and software aspects are all considered at length, while simplified procedures and predictive self-tuning schemes are shown in terms of fundamental concepts.
The level of the book is such that practising control engineers and postgraduate or final year control, engineering or cybernetics students would find it most suitable. A certain amount of basic digital/sampled data control systems terminology is assumed, although this does not mean that the reader needs to be familiar with more mathematical ideas, such as discrete transform methods or statistical theory. The text could be used as the foundation of a Masters course module, particularly if an emphasis is placed on the practical aspects of self-tuning control.
The book is an ideal source for those interested in self-tuning and adaptive control, and contains many useful suggestions for future trends, research directions and implementation/application possibilities.List of contributorsIntroduction to self-tuning control (D.W. Clarke)
The need for self-tuning
A self-tuning PI regulator for a furnace
A simple self-tuning controller
Plant models for general self-tuners
Predictive control laws
Conclusions
RLS based estimation schemes for self-tuning control (S.L. Shah and W.R. Cluett)The ordinary recursive least squares algorithm
RLS with exponential data weighting
RLS with variable forgetting
RLS with covariance resetting
RLS with matrix regularization
RLS with constant trace
RLS with dead-zone and a normalized regressor
RLS with constant trace and scaling
RLS with data-dependent updating and forgetting factor
RLS with leakage
RLS with parameter weighting
RLS with ad-hoc modifications
Conclusions
LQG based self-tuning controllers (K.J. Hunt and M.J. Grimble)Model structure
Controller design
LQG self-tuning control algorithm
Robustness of the LQG self-tuner
Convergence properties
Practical issues
Numerical algorithms in self-tuning control (C. Mohtadi)Parameter estimation
Control algorithms
Concluding remarks
Simplified algorithms for self-tuning control (K. Warwick)System definitions
Parameter estimator simplification
Model reduction methods
PID controller structure
Deadbeat control algorithms
Concluding remarks
A unified approach to adaptive control (G.C. Goodwin, R.H. Middleton and M. Salgado)The Delta operator
The system model
Unified parameter estimation
Control system design
Unified adaptive control algorithm
Some practical issues
Simulation studies
Implementation of continuous-time controllers (P.J. Gawthrop)Outline of continuous-time self-tuning
Issues in obtaining a digital algorithm
The state-variable filter
Discrete estimation of continuous-time parametersSoftware aspects of self-tuning control (P.S. Tuffs)The structure of a self-tuning controller
FORTRAN and Ada implementations
Real-time aspects
Conclusions
Multistep predictive control and adaptation (B.E. Ydstie, A.H. Kemna and L.K. Liu)Least squares estimation and forgetting factors
Multistep predictive control and constraints
The stability analysis
Application study using extended horizon control
Summary and discussion
Application of extended prediction self-adaptive control (R.M.C. DeKeyser)Extended prediction self-adaptive control
Low cost heating and ventilation control
Adaptive control of cutter suction dredgers
Conclusions
Self-tuning and self-adaptive PIP control systems (P.C. Young, M.A. Behzadi and A. Chotai)The SISO dynamic model and the non-minimal state-space form
The Proportional-Integral-Plus control system
The design procedure
Prior model identification and parameter estimation
The self-tuning PIP controller
Practical examples
Extensions
Conclusions
LQG adaptive autopilots (J. Byrne and M.R. Katebi)Mathematical modelling
The cost criterion
Control philosophy
Adaptive control philosophy
Identification scheme
Simulation results
Conclusions
Self-tuning control : Case studies (D. Peel and M. Tham)Cement mill
Turbo-tray dryer
Multivariable self-tuning controllers
Implementation aspects
Conclusions