Safe Adaptive Control : Data-driven Stability Analysis and Robust Synthesis

__Safe Adaptive Control__ gives a formal and complete algorithm for assuring the stability of a switched control system when at least one of the available candidate controllers is stabilizing. The possibility of having an unstable switched system even in the presence of a stabilizing candidate controller is demonstrated by referring to several well-known adaptive control approaches, where the system goes unstable when a large mismatch between the unknown plant and the available models exists ("plant-model mismatch instability"). Sufficient conditions for this possibility to be avoided are formulated, and a "recipe" to be followed by the control system designer to guarantee stability and desired performance is provided. The problem is placed in a standard optimization setting. Unlike the finite controller sets considered elsewhere, the candidate controller set is allowed to be continuously parametrized so that it can deal with plants with a very large range of uncertainties. Data-Driven Stability Analysis and Robust Synthesis Part I Overview of Adaptive Control 1 Overview of Adaptive Control 1 Discussion of the Adaptive Control Problem 1 Brief History Overview 4 Literature Review 4 Monograph Overview 10 Part II Safe Switching Adaptive Control 1 Safe Switching Adaptive Control: Theory 1 Background 1 Preliminaries 3 Results 8 Cost Function Example 12 Specialization to the LTI Plants 14 Treatment of the Time-Varying Plants 17 Behavioral Approach to Controller Unfalsification 20 Safe Switching Adaptive Control: Examples 1 Benchmark Example 1 Further Comparison with the Multiple Model Based Switching Control 4 Performance Improvement 13 Radial Basis Function Neural Networks in Safe Switching Control 14 Bumpless Transfer with Slow-Fast Controller Decomposition 21 Performance Improvement Using Bandpass Filters 31 Applications of Unfalsified Control 39 Automatic PID Tuning 39 Unfalsified Adaptive Spacecraft Attitude Control 43 Switching Control of Missile Autopilot 50 Switching Congestion Control for Satellite TCP/AQM Networks 56 Unfalsified Direct Adaptive Control of a Two-Link Robot Arm 75 Conclusion 1 Cover 1 Front Matter 2 Part I Overview of Adaptive Control 11 Overview of Adaptive Control 12 Discussion of the Adaptive Control Problem 12 Literature Review 15 Brief History Overview 15 Monograph Overview 21 Part II Safe Switching Adaptive Control 22 Safe Switching Adaptive Control: Theory 23 Background 23 Preliminaries 25 Results 30 Cost Function Example 34 Specialization to the LTI Plants 36 Treatment of the Time-Varying Plants 39 Behavioral Approach to Controller Unfalsification 42 Safe Switching Adaptive Control: Examples 49 Benchmark Example 49 Further Comparison with the Multiple Model Based Switching Control 52 Performance Improvement 61 Radial Basis Function Neural Networks in Safe Switching Control 62 Bumpless Transfer with Slow-Fast Controller Decomposition 69 Performance Improvement Using Bandpass Filters 79 Applications of Unfalsified Control 87 Automatic PID Tuning 87 Unfalsified Adaptive Spacecraft Attitude Control 91 Switching Control of Missile Autopilot 98 Switching Congestion Control for Satellite TCP/AQM Networks 104 Unfalsified Direct Adaptive Control of a Two-Link Robot Arm 123 Conclusion 137 Back Matter 139 Cover......Page 1 Further Comparison with the Multiple Model Based Switching Control......Page 4 Monograph Overview......Page 10 Preliminaries......Page 3 Results......Page 8 Discussion of the Adaptive Control Problem......Page 12 Radial Basis Function Neural Networks in Safe Switching Control......Page 14 Treatment of the Time-Varying Plants......Page 17 Behavioral Approach to Controller Unfalsification......Page 20 Performance Improvement......Page 13 Monograph Overview......Page 21 Performance Improvement Using Bandpass Filters......Page 31 Treatment of the Time-Varying Plants......Page 39 Unfalsified Adaptive Spacecraft Attitude Control......Page 43 Switching Control of Missile Autopilot......Page 50 Switching Congestion Control for Satellite TCP/AQM Networks......Page 56 Unfalsified Direct Adaptive Control of a Two-Link Robot Arm......Page 75 Front Matter......Page 2 Part I Overview of Adaptive Control......Page 11 Brief History Overview......Page 15 Part II Safe Switching Adaptive Control......Page 22 Background......Page 23 Preliminaries......Page 25 Results......Page 30 Cost Function Example......Page 34 Specialization to the LTI Plants......Page 36 Behavioral Approach to Controller Unfalsification......Page 42 Benchmark Example......Page 49 Further Comparison with the Multiple Model Based Switching Control......Page 52 Performance Improvement......Page 61 Radial Basis Function Neural Networks in Safe Switching Control......Page 62 Bumpless Transfer with Slow-Fast Controller Decomposition......Page 69 Performance Improvement Using Bandpass Filters......Page 79 Automatic PID Tuning......Page 87 Unfalsified Adaptive Spacecraft Attitude Control......Page 91 Switching Control of Missile Autopilot......Page 98 Switching Congestion Control for Satellite TCP/AQM Networks......Page 104 Unfalsified Direct Adaptive Control of a Two-Link Robot Arm......Page 123 Conclusion......Page 137 Back Matter......Page 139 Publication In Lncis Is Free Of Charge. 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Systemtheorie Technischer Prozesse (ist), Pfaffenwaldring 9, 70550 Stuttgart, Germany Or Professor M. Morari, Institut Fur Automatik, Eth/etl I 29, Physikstraße 3, 8092 Zurich, Switzerland, Or Directly To The Engineering Editor, Springer-verlag, Tiergartenstraße 17, 69121 Heidelberg, Germany. --book Jacket. Pt. 1. Overview Of Adaptive Control -- Pt. 2. Safe Switching Adaptive Control. Margareta Stefanovic And Michael G. Safonov. Includes Bibliographical References And Index. Annotation Safe Adaptive Control gives a formal and complete algorithm for assuring the stability of a switched control system when at least one of the available candidate controllers is stabilizing. The possibility of having an unstable switched system even in the presence of a stabilizing candidate controller is demonstrated by referring to several well-known adaptive control approaches, where the system goes unstable when a large mismatch between the unknown plant and the available models exists ("plant-model mismatch instability"). Sufficient conditions for this possibility to be avoided are formulated, and a "recipe" to be followed by the control system designer to guarantee stability and desired performance is provided. The problem is placed in a standard optimization setting. Unlike the finite controller sets considered elsewhere, the candidate controller set is allowed to be continuously parametrized so that it can deal with plants with a very large range of uncertainties