Neural-network-based optimal control for a class of nonlinear cdiscrete-time systems with control constraints using the citerative GDHP algorithm

In this paper, a neural-network-based optimal control scheme for a class of nonlinear discrete-time systems with control constraints is proposed. The iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to design the optimal controller with convergence proof. Three neural networks are used to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the controlled nonlinear discrete-time system, respectively. A simulation study is carried out to demonstrate the effectiveness of the present approach in dealing with the nonlinear constrained optimal control problem.