Adaptive Control

Adaptive Control provides techniques for automatic, real-time adjustments in controller parameters with a view to achieving and/or maintaining a desirable level of system performance in the presence of unknown or variable process parameters.

Many aspects of the field are dealt with in coherent and orderly fashion, starting with the problems posed by system uncertainties and moving on to the presentation of solutions and their practical significance. Within the general context of recent developments, the book looks at:

• synthesis and analysis of parameter adaptation algorithms;

• recursive plant-model identification in open and closed loop;

• robust digital control for adaptive control;

• direct and indirect adaptive control; and

• practical aspects and applications.

To reflect the importance of digital computers for the application of adaptive control techniques, discrete-time aspects are emphasized. To guide the reader, the book contains various applications of adaptive control techniques.

Provides the reader with a comprehensive guide to the ideas of adaptive control and the solutions it provides to system uncertainties Gives the reader a comparative overview of the importance of various digital computer configurations in implementing adaptive control Provides practical advice on the synthesis of parameter adaptation algorithms including robust algorithms

Autorentext

Bernard Brogliato was born in 1963, graduated from Ecole Normale Supérieure de Cachan (France), Mechanical Engineering Dept., Ph.D. and Habilitation degree in Automatic Control from Grenoble Institute of Technology in 1991 and 1995 respectively. He is Senior Researcher at INRIA Grenoble Rhône-Alpes. Research interests: non-smooth dynamical systems (analysis, control and observation, numerics), impact and contact mechanics, digital sliding-mode control. Wrote about 90 articles in international journals in the fields of Systems and Control, Mechanical Engineering, and Applied Mathematics. Also authored and co-authored five monographs. He was Associate Editor for Automatica (1999-2008), is Associate Editor for Nonlinear Analysis Hybrid Systems, and ASME Journal of Computational and Nonlinear Dynamics. Rogelio Lozano was born in Monterrey Mexico, on July 12, 1954. He received the B.S. degree in electronic engineering fromthe National Polytechnic Institute of Mexico in 1975, the M.S. degree in electrical engineering from Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico in 1977, and the Ph.D. degree in automatic control from Laboratoire d'Automatique de Grenoble, France, in 1981. He joined the Department of Electrical Engineering at CINVESTAV, Mexico, in 1981 where he worked until 1989. He was Head of the Section of Automatic Control from June 1985 to August 1987. He has held visiting positions at the University of Newcastle, Australia, from November 1983 to November 1984, NASA Langley Research Center VA, from August 1987 to August 1988, and Laboratoire d'Automatique de Grenoble, France, from February 1989 to July 1990. Since 1990 he is a CNRS (Centre National de la Recherche Scientifique) Research Director at University of Technology of Compiègne, France. He was Associate Editor of Automatica in the period 1987-2000. He is associate Editor of the Journal of Intelligentand Robotics Systems since 2012 and Associate Editor in the Int. J. of Adaptive Control and Signal Processing since 1988. Bernhard Maschke is Professor of Automatic Control of the University Claude Bernard of Lyon, Villeurbanne, France since 2000. The main streamline of his research is the nonlinear and passivity-based control of complex physical systems. He is one of the main initiators of the Port Hamiltonian formalism which bases the modelling, simulation and control of complex physical systems on network theory and thermodynamic theory. He has used this formalism for complex spatial mechanisms and in the mechatronic context of automotive applications and more recently to chemical enginneering processes and various multi-physics and multi-scale systems such as an adsorption process, a fuel cell or an Ion Polymer Metal Composite. Olav Egeland is a graduate of the Norwegian University of Science and Technology (NTNU), where he is professor of production automation. He was at Marine Cybernetics AS 2004-2011 as co-founder. He has received the Automatica Prize Paper Award and the Outstanding Paper Award of IEEE Trans. Control Systems Technology, and has been Associate Editor of IEEE Trans. Automatic Control and European Journal of Control. His research is on modelling, simulation and control of mechanical systems with applications to robotics and offshore systems.

Inhalt
to Adaptive Control.- Adaptive Control - Why?.- Adaptive Control Versus Conventional Feedback Control.- Basic Adaptive Control Schemes.- Examples.- A Brief Historical Note.- Concluding Remarks.- Discrete Time System Models for Control.- Deterministic Environment.- Stochastic Environment.- Concluding Remarks.- Problems.- Parameter Adaptation Algorithms - Deterministic Environment.- The Problem.- PAA - Examples.- Stability of Parameter Adaptation Algorithms.- Parametric Convergence.- Concluding Remarks.- Problems.- Parameter Adaptation Algorithms - Stochastic Environment.- Effect of Stochastic Disturbances.- The Averaging Method.- The Martingale Approach.- The Frequency Domain Approach.- Concluding Remarks.- Problems.- Recursive Plant Model Identification in Open Loop.- Recursive Identification in the Context of System Identification.- Structure of Recursive Parameter Estimation Algorithms.- Identification Methods (Type I).- Validation of the Models Identified with Type I Methods.- Identification Methods (Type II).- Validation of the Models Identified with Type II Methods.- Selection of the Pseudo Random Binary Sequence.- Model Order Selection.- An Example: Identification of a Flexible Transmission.- Concluding Remarks.- Problems.- Adaptive Prediction.- The Problem.- Adaptive Prediction - Deterministic Case.- Adaptive Prediction - Stochastic Case.- Concluding Remarks.- Problems.- Digital Control Strategies.- Canonical Form for Digital Controllers.- Pole Placement.- Tracking and Regulation with Independent Objectives.- Tracking and Regulation with Weighted Input.- Minimum Variance Tracking and Regulation.- Generalized Predictive Control.- Linear Quadratic Control.- Concluding Remarks.- Problems.- Robust Digital Control Design.- The Robustness Problem.- The Sensitivity Functions.- Robust Stability.- Definition of Templates for the Sensitivity Functions.- Properties of the Sensitivity Functions.- Shaping the Sensitivity Functions.- Example: Robust Digital Control of a Flexible Transmission.- Concluding Remarks.- Problems.- Recursive Plant Model Identification in Closed Loop.- The Problem.- Closed Loop Output Error Algorithms.- Filtered Open Loop Recursive Identification Algorithms.- Frequency Distribution of the Asymptotic Bias in Closed Loop Identification.- Validation of Models Identified in Closed Loop.- Comparative Evaluation of the Various Algorithms.- Concluding Remarks.- Problems.- Robust Parameter Estimation.- The Problem.- Input/Output Data Filtering.- Effect of Disturbances.- PAA with Dead Zone.- PAA with Projection.- Data Normalization.- A Robust Parameter Estimation Scheme.- Concluding Remarks.- Problems.- Direct Adaptive Control.- Adaptive Tracking and Regulation with Independent Objectives.- Adaptive Tracking and Regulation with Weighted Input.- Adaptive Minimum Variance Tracking and Regulation.- Adaptive Generalized Minimum Variance Control.- Robust Direct Adaptive Control.- An example.- Concluding Remarks.- Problems.- Indirect Adaptive Control.- Adaptive Pole Placement.- Robust Indirect Adaptive Control.- Adaptive Generalized Predictive Control.- Adaptive Linear Quadratic Control.- Iterative Identification in Closed Loop and Controller Redesign.- An Example.- Concluding Remarks.- Practica…