Motion control of chaotic permanent-magnet synchronous motor servo system with neural network–based disturbance observer

The permanent-magnet synchronous motor system will display a variety of chaotic phenomenon when its parameters or external inputs satisfy certain condition, and thus its performance would be deteriorated. Therefore, chaos should be suppressed or eliminated. In this article, a practical method which combines adaptive robust control with a single-layer neural network–based disturbance observer is proposed for elimination of the chaos and high-performance motion control of permanent-magnet synchronous motor. The proposed controller not only accounts for the load torque disturbance but also takes the parametric uncertainties into account. A single-layer neural network–based disturbance observer is designed to estimate the disturbance while an adaptive control law is designed to estimate the parameters respectively. Then, all the estimated values are used in the feedforward cancelation item in the controller via a backstepping technique. Lyapunov’s method is used to prove the stability of the novel control scheme. Sufficient comparative simulation results are obtained to validate the effectiveness of the proposed control strategy.