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. This approach avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in efficient controllers. The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control scheme, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Simulations illustrate the effectiveness of the synthesized controllers.