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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.

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