Adaptive tracking control for an uncertain chaotic permanent magnet synchronous motor based on fuzzy neural networks

In this paper, an adaptive controller is developed to suppress chaos and track the desired speed in an uncertain chaotic permanent magnet synchronous motor (PMSM) drive system. The controller consists of computational and supervisory control schemes. The computational controller, based on fuzzy neural networks, is used to approximate the unknown nonlinear control signal, while the supervisory controller is employed to attenuate the approximation error effects of the neural network and ensure the system is robust. Simulation results demonstrate that the proposed controller can successfully quash chaotic oscillation in a PMSM and allow speeds to follow the desired trajectory despite the existence of uncertainties.