Barrier Lyapunov Functions-Based Adaptive Neural Control for Permanent Magnet Synchronous Motors With Full-State Constraints

Considering the requirement of high accuracy and nonlinear problems in drive systems, a novel adaptive position tracking control approach based on neural networks is presented for permanent magnet synchronous motors with full-state constraints. The neural networks technique is employed to approximate the unknown nonlinear functions. Then, the barrier Lyapunov functions are used to restrict the state variables within a bounded compact set to improve the property of system. The proposed adaptive neural network controllers can guarantee that all closed-loop variables are bounded, and the full state variables do not exceed their constraint spaces. Simulation results show the effectiveness and the potentials of the theoretic results obtained.