System Identification and Robust Control: A Case Study Approach [Paperback]

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The present text Steen T0ffner-Clausen deals with both system identification and robust control. It provides a very comprehensive tutorial introduction to some of the most difficult topics in robust control theory before considering applications problems. Traditional Hoo robust control design concepts for multivariable systems are first considered and the problems of robust stability and performance are discussed. The following chapter introduces the idea of the structured singular value and applies this to both analysis and synthesis problems. The author manages to provide a very straightforward introduction to this subject and also introduces some new ideas.1. Introduction.- 1.1 The Organization of the Book.- I. Robust Control  Theory and Design.- 2. Introduction to Robust Control.- 3. Spaces and Norms in Robust Control Theory.- 4. Robust Control Design using Singular Values.- 5. Robust Control Design using Structured Singular Values.- 6. Mixed ? Control of a Compact Disc Servo Drive.- 7. ? Control of an Ill-Conditioned Aircraft.- II. System Identification and Estimation of Model Error Bounds.- 8. Introduction to Estimation Theory.- 9. Classical System Identification.- 10. Orthonormal Filters in System Identification.- 11. The Stochastic Embedding Approach.- 12. Estimating Uncertainty using Stochastic EmbeddinglCÞ