Description
Book SynopsisDiscrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller.
Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems
The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). The synthesized discrete-time optimal controller can be directly implemented in real-time systems. The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control approach, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamic
Table of Contents
Introduction. Mathematical Preliminaries. Inverse Optimal Control: A Passivity Approach. Inverse Optimal Control: A Control Lyapunov Function Approach, Part I. Inverse Optimal Control: A Control Lyapunov Function Approach, Part II. Neural Inverse Optimal Control. Glycemic Control of Type 1 Diabetes Mellitus Patients. Conclusions. References.
