Discrete-Time Inverse Optimal Control for Nonlinear Systems

Discrete-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 dynamics and disturbances. Learn from Simulations and an In-Depth Case Study The authors include a variety of simulations to illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems. An in-depth case study applies the control schemes to glycemic control in patients with type 1 diabetes mellitus, to calculate the adequate insulin delivery rate required to prevent hyperglycemia and hypoglycemia levels. The discrete-time optimal and robust control techniques proposed can be used in a range of industrial applications, from aerospace and energy to biomedical and electromechanical systems. Highlighting optimal and efficient control algorithms, this is a valuable resource for researchers, engineers, and students working in nonlinear system control.

Introduction Inverse Optimal Control via Passivity Inverse Optimal Control via CLF Neural Inverse Optimal Control Motivation Mathematical Preliminaries Optimal Control Lyapunov Stability Robust Stability Analysis Passivity Neural Identification Inverse Optimal Control: A Passivity Approach Inverse Optimal Control via Passivity Trajectory Tracking Passivity-based Inverse Optimal Control for a Class of Nonlinear Positive Systems Conclusions Inverse Optimal Control: A CLF Approach, Part I Inverse Optimal Control via CLF Robust Inverse Optimal Control Trajectory Tracking Inverse Optimal Control CLF-based Inverse Optimal Control for a Class of Nonlinear Positive Systems Conclusions Inverse Optimal Control: A CLF Approach, Part II Speed-Gradient Algorithm for the Inverse Optimal Control Speed-Gradient Algorithm for Trajectory Tracking Trajectory Tracking for Systems in Block-Control Form Conclusions Neural Inverse Optimal Control Neural Inverse Optimal Control Scheme Block-Control Form: A Nonlinear Systems Particular Class Conclusions Glycemic Control of Type 1 Diabetes Mellitus Patients Introduction Passivity Approach CLF Approach Conclusions Conclusions References