Robust Control Design Using H- Methods

Methods are illustrated by way of 2 practical problems concerning a missile auto pilot design problem and an active noise control problem The authors are all experts in the areas of robust control theory Both deterministic and stochastic uncertainty models are considered

Autorentext

Daoyi Dong is currently a Scientia Associate Professor at the University of New South Wales, Canberra, Australia, and an Australian Research Council Future Fellow. His research interests include quantum control and machine learning. He has published more than 100 journal papers and more than 40 conference papers. Associate Professor Dong was awarded an ACA Temasek Young Educator Award by The Asian Control Association, a Humboldt Fellowship by Alexander von Humboldt Foundation, and is a recipient of a Future Fellowship, an International Collaboration Award and an Australian Post-Doctoral Fellowship from the Australian Research Council. He served as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (2015-2021) and currently a Technical Editor of IEEE/ASME Transactions on Mechatronics, an Associate Editor of IEEE Transactions on Cybernetics, and a Guest Editor of Annual Reviews in Control. Ian R. Petersen is currently a professor at the Australian National University. He held an Australian Research Council Professorial Fellowship from 2005 to 2007, an Australian Research Council Federation Fellowship from 2007 to 2012, and an Australian Research Council Laureate Fellowship from 2012 to 2016. He has served as an Associate Editor for the IEEE Transactions on Automatic Control, Systems and Control Letters, Automatica, and SIAM Journal on Control and Optimization. Currently he is an Editor for Automatica in the area of optimization in systems and control. He is a fellow of the IFAC, the IEEE and the Australian Academy of Science. His main research interests are in robust control theory, quantum control theory and stochastic control theory. Ian Petersen was elected IFAC Council Member for the 2014-2017 and 2018-2021 Trienniums. He was also elected to be a member of the IEEE Control Systems Society Board of Governors for the periods 2011-2013 and 2015-2017. He was Vice-president for Technical Activity for the Asian Control Association and was General Chair of the 2012 Australia Control Conference. He was General Chair of the 2015 IEEE Multi-Conference on Systems and Control.

Zusammenfassung
This is a unified collection of important recent results for the design of robust controllers for uncertain systems, primarily based on H8 control theory or its stochastic counterpart, risk sensitive control theory. Two practical applications are used to illustrate the methods throughout.

Inhalt

1. Introduction.- 1.1 The concept of an uncertain system.- 1.2 Overview of the book.- 2. Uncertain systems.- 2.1 Introduction.- 2.2 Uncertain systems with norm-bounded uncertainty.- 2.3 Uncertain systems with integral quadratic constraints.- 2.4 Stochastic uncertain systems.- 3. H? control and related preliminary results.- 3.1 Riccati equations.- 3.2 H? control.- 3.3 Risk-sensitive control.- 3.4 Quadratic stability.- 3.5 A connection between H? control and the absolute stabilizability of uncertain systems.- 4. The S-procedure.- 4.1 Introduction.- 4.2 An S-procedure result for a quadratic functional and one quadratic constraint.- 4.3 An S-procedure result for a quadratic functional and k quadratic constraints.- 4.4 An S-procedure result for nonlinear functionals.- 4.5 An S-procedure result for averaged sequences.- 4.6 An S-procedure result for probability measures with constrained relative entropies.- 5. Guaranteed cost control of time-invariant uncertain systems.- 5.1 Introduction.- 5.2 Optimal guaranteed cost control for uncertain linear systems with norm-bounded uncertainty.- 5.3 State-feedback minimax optimal control of uncertain systems with structured uncertainty.- 5.4 Output-feedback minimax optimal control of uncertain systems with unstructured uncertainty.- 5.5 Guaranteed cost control via a Lyapunov function of the Lur'e-Postnikov form.- 5.6 Conclusions.- 6. Finite-horizon guaranteed cost control.- 6.1 Introduction.- 6.2 The uncertainty averaging approach to state-feedback minimax optimal control.- 6.3 The uncertainty averaging approach to output-feedback optimal guaranteed cost control.- 6.4 Robust control with a terminal state constraint.- 6.5 Robust control with rejection of harmonic disturbances.- 7. Absolute stability, absolute stabilization andstructured dissipativity.- 7.1 Introduction.- 7.2 Robust stabilization with a Lyapunov function of the Lur'e-Postnikov form.- 7.3 Structured dissipativity and absolute stability for nonlinear uncertain systems.- 7.4 Conclusions.- 8. Robust control of stochastic uncertain systems.- 8.1 Introduction.- 8.2 H? control of stochastic systems with multiplicative noise.- 8.3 Absolute stabilization and minimax optimal control of stochastic uncertain systems with multiplicative noise.- 8.4 Output-feedback finite-horizon minimax optimal control of stochastic uncertain systems with additive noise.- 8.5 Output-feedback infinite-horizon minimax optimal control of stochastic uncertain systems with additive noise.- 8.6 Conclusions.- 9. Nonlinear versus linear control.- 9.1 Introduction.- 9.2 Nonlinear versus linear control in the absolute stabilizability of uncertain systems with structured uncertainty.- 9.3 Decentralized robust state-feedback H? control for uncertain large-scale systems.- 9.4 Nonlinear versus linear control in the robust stabilizability of linear uncertain systems via a fixed-order output-feedback controller.- 9.5 Simultaneous H? control of a finite collection of linear plants with a single nonlinear digital controller.- 9.6 Conclusions.- 10. Missile autopilot design via minimax optimal control of stochastic uncertain systems.- 10.1 Introduction.- 10.2 Missile autopilot model.- 10.3 Robust controller design.- 10.4 Conclusions.- 11. Robust control of acoustic noise in a duct via minimax optimal LQG control.- 11.1 Introduction.- 11.2 Experimental setup and modeling.- 11.3 Controller design.- 11.4 Experimental results.- 11.5 Conclusions.- A. Basic duality relationships for relative entropy.- B. Metrically transitive transformations.- References.