Control of Chaotic Permanent Magnet Synchronous Motor Using Adaptive Nonlinear-in-Parameter Approximator

Abstract

An adaptive nonlinear-in-parameter (NIP) approximator-based control approach is proposed to control of a chaotic permanent magnet synchronous motor (PMSM) drive system. It proposes fuzzy wavelet network (FWN) as an adaptive nonlinear-in-parameter (NIP) approximator to represent the model of the uncertain dynamics. Then, it uses dynamic surface control (DSC) approach to design controller. The dilation and the translation of the wavelet functions and the weights of the network are learned online based on the adaptive laws. Stability analysis guarantees that all of the closed-loop signals are semi-globally uniformly ultimately bounded. Also, proper selection of the design parameters results in small tracking error in the vicinity of the origin. Compared with the conventional backstepping-based approaches, in this work, both of the "explosion of complexity" and "explosion of learning parameters" are eliminated, simultaneously. Furthermore, the availability and boundedness of all derivatives of the desired trajectory are not required for controller design. Simulation results verify the ability of the proposed controller to suppress chaos in the PMSM drive systems.