This book provides techniques to produce robust, stable and useable solutions to problems of H-infinity and H2 control in high-performance, non-linear systems for the first time. The book is of importance to control designers working in a variety of industrial systems. Case studies are given and the design of nonlinear control systems of the same caliber as those obtained in recent years using linear optimal and bounded-norm designs is explained.

The authors present algorithms for H2 and H-infinity design for nonlinear systems which provide solution techniques which can be implemented in real systems; neural networks are used to solve the nonlinear control design equations. Constraints on the control actuator inputs are dealt with. Results are proven to give confidence and performance guarantees. The algorithms can be used to obtain practical controllers. Nearly optimal applications to constrained-state and mimimum-time problems are discussed as is discrete-time design for digital controllers. Nonlinear H2/H-infinity Constrained Feedback Control is of importance to control designers working in a variety of industrial systems. Case studies are given and the design of nonlinear control systems of the same caliber as those obtained in recent years using linear optimal and bounded-norm designs is explained. The book will also be of interest to academics and graduate students in control systems as a complete foundation for H2 and H-infinity design.

In this book, approximate dynamic programming (ADP) designs based on adaptive critic structures are developed to solve the discrete-time optimal control problems in which the state and action spaces are continuous. This work considers linear discrete-time systems as well as nonlinear discrete- time systems that are affine in the input. This work resulted in forward-in-time reinforcement learning algorithms that converge to the solution of the Generalized Algebraic Riccati Equation (GARE) for linear systems. For the nonlinear case, a forward-in-time reinforcement learning algorithm is presented that converges to the solution of the associated Hamilton-Jacobi Bellman equation (HJB).