Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

摘要: This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with minimum weights. The BLDCM contains parameter perturbation, chaotic behavior, and uncertainty. With help of radial basis function (RBF) to approximate unknown nonlinear functions, law is established overcome uncertainty gain. By introducing RBF technology into design, a robust scheme developed. It proved that proposed can guarantee all signals in closed-loop are globally uniformly bounded, tracking error converges small neighborhood origin. Simulation results provided show works well suppressing perturbation.