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

In this paper a new method for the optimal design of suspension parameters of flexible mutibody vehicle model is presented. This method is developed based on the interface between the ADAMS software and the improved genetic algorithms in this paper. The 44 degrees of freedom flexible muitibody and the 33 degrees of freedom multi-rigid bodies vehicle model are chosen as examples for the optimal design of suspension parameters. The results show that the optimization method developed in this paper is better than the other methods.

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