Adaptive NN finite-time tracking control for PMSM with full state constraints

Abstract

This paper addresses the tracking control problem for a class of permanent magnet synchronous motors (PMSM) systems with asymmetric full-state constraints. To overcome the difficulty of controller design caused by the state constraint problems of system, a nonlinear transformation function i introduced to transform the state constraint problems into a non-constraint problems. Then, radial basis function neural networks (NN) is employed to approximate the uncertainties in the system. In addition, by combining the techniques of command filter and finite-time control, a novel virtual control signal and modified error compensation signals are proposed to construct the actual control law, which solves the problems of “explosion of complexity” and “singularity”. It is shown that all signals of the closed-loop system are bounded and the tracking error remains in a small neighborhood of the origin in finite time. Finally, the simulations show the performance and feasibility of the proposed control scheme.