Adaptive-Neuro-Learning Tracking Control for the Permanent Magnet Synchronous Motor with Full-State Prescribed Performances and Time Delays

Abstract

High-performance tracking control is essential for permanent magnet synchronous motors in the perturbed environment. Given this, a new hybrid controller is proposed in this study for a permanent magnet synchronous motor with load disturbances as well as time delays. First, a new prescribed performance method is proposed to achieve the full-state performance constraints with load disturbances. Second, a time-varying filter is proposed for the first time to avoid the “complexity explosion” problem of the backstepping method while guaranteeing the convergence of the filtering error. Third, by combining Lyapunov–Krasovskii functionals with adaptive neural networks, the time-delay disturbance and unknown nonlinear dynamics of the control system have been solved. The stability analysis proves that all signals in the closed-loop system are bounded. To show the effectiveness of the intelligent controller, the comparison simulations are given to confirm the advantages of the proposed adaptive neural control scheme.