Discrete-Time Inverse Optimal Control for Nonlinear Systems

This book presents a novel inverse optimal control approach for stabilization and trajectory tracking of discrete-time nonlinear systems, avoiding the need to solve the associated Hamilton-Jacobi-Bellman equation, and minimizing a cost functional, resulting in efficient controllers. Additionally, the book proposes the use of recurrent neural networks as a tool to model discrete-time nonlinear systems; such models combined with the inverse optimal control constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Different simulations illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems.

Edgar N. Sanchez is a researcher at CINVESTAV-IPN, Guadalajara Campus, Mexico. He was granted a U.S. National Research Council Award as a research associate at NASA Langley Research Center (January 1985-March 1987). He is also a member of the Mexican National Research System (promoted to the highest rank, III, in 2005), the Mexican Academy of Science, and the Mexican Academy of Engineering. He has published more than 100 technical papers in international journals and conferences, and has served as reviewer for various international journals and conferences. His research interest centers on neural networks and fuzzy logic as applied to automatic control systems.

Fernando Ornelas-Tellez is currently a professor of electrical engineering at Michoacan University of Saint Nicholas of Hidalgo, Mexico. His research interests center on neural control, direct and inverse optimal control, passivity and their applications to biomedical systems, electrical machines, power electronics, and robotics.