Optimal and Robust Control

There are many books on advanced control for specialists, but not many present these topics for non-specialists. Assuming only a basic knowledge of automatic control and signals and systems, this second edition of Optimal and Robust Control offers a straightforward, self-contained handbook of advanced topics and tools in automatic control.

The book deals with advanced automatic control techniques, paying particular attention to robustness-the ability to guarantee stability in the presence of uncertainty. It explains advanced techniques for handling uncertainty and optimizing the control loop. It also details analytical strategies for obtaining reduced order models. The authors then propose using the Linear Matrix Inequality (LMI) technique as a unifying tool to solve many types of advanced control problems. Topics covered in the book include,

• LQR and H∞ approaches
• Kalman and singular value decomposition
• Open-loop balancing and reduced order models
• Closed-loop balancing
• Positive-real systems, bounded-real systems, and imaginary-negative systems
• Criteria for stability control
• Time-delay systems

This easy-to-read text presents the essential theoretical background and provides numerous examples and MATLAB® exercises to help the reader efficiently acquire new skills. Written for electrical, electronic, computer science, space, and automation engineers interested in automatic control, this book can also be used for self-study of for a one-semester course in robust control.

This fully renewed second edition of the book also includes new fundamental topics such as Lyapunov functions for stability, variational calculus, formulation in terms of optimization problems of matrix algebraic equations, negative-imaginary systems, and time-delay systems.

Luigi Fortuna is a Full Professor of System Theory with the Università degli Studi di Catania, Catania, Italy. He has published more than 600 scientific papers regarding robust control, nonlinear science and complexity, chaos, cellular neural networks, softcomputing strategies for control and robotics, micronanosensor and smart devices for control, and nanocellular neural networks modeling.

Mattia Frasca is presently as Associate Professor at the University of Catania, Italy and teaches Process Control and Complex Adaptive Systems. His scientific interests include linear systems, nonlinear dynamics, analysis and control of complex systems, and bio-inspired robotics.

Arturo Buscarino is currently a researcher in Automatic Control at the University of Catania, Italy. He is IEEE Senior Member. His scientific interests include nonlinear circuits and systems, chaos and synchronization, memristors, fuzzy logic, control systems, Cellular Nonlinear Networks, and plasma engineering.