Model Identification and Adaptive Control

This book is based on a workshop entitled.: Model " Identification and Adap­ tive Control: From Windsurfing to Telecommunications" held in Sydney, Aus­ tralia, on December 16, 2000. The workshop was organized in honour of Pro­ fessor Brian (BDO) Anderson in recognition of his seminal contributions to systems science over the past 4 decades. . The chapters in the book have been written by colleagues, friends and stu­ dents of Brian Anderson. A central theme of the book is the inter relationship between identification and the use of models in real world applications. This theme has underpinned much of Brian Anderson's own contributions. The book reflects on these contributions as well as makirig important statements about possible future research directions. The subtitle of the book (From Windsurfing to Telecommunications) rec­ ognizes the fact that many common life experiences, such as those we en­ counter when learning to ride a windsurfer are models for design methods that can be used on real world advanced technological control problems. In­ deed, Brian Anderson extensively explored this link in his research work.

I. General Aspects of Model Identification.- 1 System Identification - General Aspects and Structure.- 1.1 Introduction.- 1.2 Structure Theory.- 1.3 Estimation for a given subclass.- 1.4 Model selection.- 1.5 Linear non-mainstream cases.- 1.6 Non-linear systems.- 1.7 Past, present state and perspectives of system identification.- References.- 2 Singularity Issues Associated with Closed Loop Identification.- 2.1 Introduction.- 2.2 Frequency Domain Estimation of Complimentary Sensitivity.- 2.3 Plant Identification via Parametric Modelling of the Complementary Sensitivity.- 2.4 Simulation Example.- 2.5 Modelling of Plant via Nonparametric Methods.- 2.6 Modified Indirect Estimation of G.- 2.7 Heuristic Analysis of Finite Sample Properties.- 2.8 Rigorous Analysis of Bias and Variance.- 2.9 Simulation.- 2.10 Conclusion.- References.- 3 Mapping Parametric Confidence Ellipsoids to Nyquist Plane for Linearly Parametrized Transfer Functions.- 3.1 Introduction.- 3.2 Problem statement.- 3.3 Linear algebra preliminaries.- 3.4 Image of D in the Nyquist plane.- 3.5 Inverse image of L.- 3.6 Probability level linked to the confidence region L.- 3.7 Case of not linearly parametrized model structures.- 3.8 Simulation example.- 3.9 Conclusions.- References.- II. Interactions Between Model Identification and Intended Model Use.- 4 On the Use of Real Data for Controller Reduction.- 4.1 Introduction.- 4.2 Notations.- 4.3 The algorithms.- 4.4 Plant model identification in closed loop.- 4.5 Properties of the estimated reduced order controllers.- 4.6 A simulation example.- 4.7 Experimental results on an active suspension.- 4.8 Conclusion.- References.- 5 Making a Nonlinear Observer Adaptive.- 5.1 Introduction.- 5.2 Arcak observer.- 5.3 Van der Pol oscillator with a parameter.- 5.4 Adaptation: a MRAC scheme.- 5.5 Adaptation: a gradient scheme.- 5.6 Simulations and discussion.- References.- 6 Sensor Adaptive Target Tracking over Variable Bandwidth Networks.- 6.1 Introduction.- 6.2 Problem Formulation.- 6.3 Illustrative Applications.- 6.4 Optimal Algorithms.- 6.5 Sub-optimal Algorithms.- 6.6 Numerical Examples.- 6.7 Extensions.- III. Illustrative Applications of Identification and Control.- 7 Adaptive Servo Control of Large Antenna Structures.- 7.1 Introduction.- 7.2 System description.- 7.3 Servo controller design objectives.- 7.4 Classical cascaded feedback.- 7.5 Adaptive position control.- 7.6 Conclusion.- References.- 8 Quantizer Optimization: Application of Passivity in Telecommunications.- 8.1 Introduction.- 8.2 Problem Formulation.- 8.3 Passivity Concepts and Definitions.- 8.4 Stability and Error Propagation.- 8.5 Main Stability Results.- 8.6 Conclusions.- References.- 9 Connecting Steiglitz-McBride Identification, Active Noise Control, and Coefficient Filtering to a Common Framework.- 9.1 Introduction.- 9.2 Technical Background.- 9.3 Steiglitz-McBride Algorithms.- 9.4 Active Noise Control and the Filtered-u Algorithms.- 9.5 Coefficient Filtering and Update Smoothing.- 9.6 Conclusion.- References.- IV. Fundamental Design Issues in Adaptive Control.- 10 Iterative Adaptive Control: Windsurfing with Confidence.- 10.1 Introduction.- 10.2 Iterative Adaptive Control.- 10.3 Parametrization and Performance.- 10.4 Uncertainty Model Unfalsification.- 10.5 Controller Unfalsification.- 10.6 Iterative Controller Unfalsification.- 10.7 Concluding Remarks.- References.- 11 Model Quality for the Windsurfer Scheme of Adaptive Control.- 11.1 Introduction.- 11.2 Internal Model Control for Stable Systems.- 11.3 Models for Robust Stability and Performance.- 11.4 Conclusions.- 11.5 Coda.- References.- 12 Cheap Control Fundamental Limitations: The Linear Time-Varying Case.- 12.1 Introduction.- 12.2 Linear Time-Varying Systems.- 12.3 Cheap LQR Problem.- 12.4 Main Result.- 12.5 Example.- 12.6 Conclusion.- References.- V. Appendix.