Neural-network-based optimal control for a class of complex-valued nonlinear systems with input saturation

This paper proposes an optimal control scheme based on adaptive dynamic programming (ADP) algorithm for complex-valued systems with input saturation. The equivalence transformation is used to obtain the real dynamic system. Then the performance index function is defined. Based on the transformed system, an ADP optimal control method is established. The update methods for critic network neural network and action network are given. It is proved that the closed-loop system is uniformly ultimately bounded based on Lyapunov approach. Finally, the simulation study was given to show the effectiveness of the proposed optimal control scheme.