Finite-time adaptive NN control for permanent magnet synchronous motors with full-state constraints

Abstract

This paper proposes a finite-time adaptive neural network (NN) position tracking control method for the permanent magnet synchronous motor (PM SM) servo system with full-state constraints. NNs are utilized to compensate the parameter uncertainties and load torque disturbances of the PMSM servo system. Multiple barrier Lyapunov functions are introduced in the backstepping based control design, such that the rotor position, the rotor angular velocity and the currents of the d-q axis are constrained in given compact sets. In addition, the finite-time control technique is adopted to the control design, which can accelerate the convergence speed and improve the robustness of the PMSM servo system. Under the proposed control algorithm, the position tracking control is realized in finite time, and the tracking error can converge to a small neighborhood of the origin. Finally, simulation results are presented to show the effectiveness of the developed control approach, and some comparisons are given to show the rapid and accurate position tracking control performance.