Neuro-optimal control for a class of unknown nonlinear dynamic systems using SN-DHP technique

In this paper, the adaptive dynamic programming (ADP) approach is utilized to design a neural-network-based optimal controller for a class of unknown discrete-time nonlinear systems with quadratic cost function. To begin with, a neural network identifier is constructed to learn the unknown dynamic system with stability proof. Then, the iterative ADP algorithm is developed to handle the nonlinear optimal control problem with convergence analysis. Moreover, the single network dual heuristic dynamic programming (SN-DHP) technique, which eliminates the use of action network, is introduced to implement the iterative ADP algorithm. Finally, two simulation examples are included to illustrate the effectiveness of the present approach.