Adaptive neural networks optimal control of permanent magnet synchronous motor system with state constraints

Abstract

This paper investigates an adaptive neural network (NN) optimal control problem for a permanent magnet synchronous motor (PMSM) system. The addressed PMSM system contains unknown nonlinear dynamics and constraint states. The neural networks (NNs) are employed to identify the unknown nonlinear dynamics and by constructing barrier performance index functions and barrier Lyapunov functions, an adaptive NN optimal control method is developed under the framework of the actor-critic architecture and backstepping control technique. It is proved that the presented optimal control strategy can not only ensure the closed-loop system stable, but also guarantee the angular velocity, stator current and other state variables of PMSM are within given bounds. Furthermore, it can minimize the performance index functions. The effectiveness of the developed NN adaptive optimal controller is verified by computer simulation results.