Robust Control of Uncertain Dynamic Systems ― A Linear State Space Approach

This textbook aims to provide a clear understanding of the various tools of analysis and design for robust stability and performance of uncertain dynamic systems. In model-based control design and analysis, mathematical models can never completely represent the “real world” system that is being modeled, and thus it is imperative to incorporate and accommodate a level of uncertainty into the models. This book directly addresses these issues from a deterministic uncertainty viewpoint and focuses on the interval parameter characterization of uncertain systems. Various tools of analysis and design are presented in a consolidated manner. This volume fills a current gap in published works by explicitly addressing the subject of control of dynamic systems from linear state space framework, namely using a time-domain, matrix-theory based approach.

This book also:

Presents and formulates the robustness problem in a linear state space model framework.

Illustrates various systems level methodologies with examples and applications drawn from aerospace, electrical and mechanical engineering.

Provides connections between lyapunov-based matrix approach and the transfer function based polynomial approaches.

Robust Control of Uncertain Dynamic Systems: A Linear State Space Approach is an ideal book for first year graduate students taking a course in robust control in aerospace, mechanical, or electrical engineering.

Professor Yedavalli has been teaching the robust control course at Ohio State for many years. Having not found a text that meets his needs, he developed his own course notes. The course in robust control is offered at the first year graduate level at universities with a graduate level engineering program. Professor Yedavalli believes that there is a need for a book that concentrates on the time domain state space approach and uses quadratic stability (lyapunov based) methodology. The books currently available take a frequency domain (transfer function based) approaches such as H-infinity and Mu synthesis.