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

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.