Optimal control of unknown discrete-time nonlinear systems with constrained inputs using GDHP technique

The adaptive dynamic programming (ADP) approach is employed to design an optimal controller for unknown discrete-time nonlinear systems with control constraints. First, a neural network is constructed to identify the unknown dynamical system with stability proof. Then, the iterative ADP algorithm is developed to solve the optimal control problem with convergence analysis. Moreover, two other neural networks are introduced to approximate the cost function and its derivative and the control law, under the framework of globalized dual heuristic programming technique. Finally, two simulation examples are included to verify the theoretical results.