Nonlinear uncertainty observer for AC motor control using the radial basis function networks

Some kinds of speed control loops can be easily designed based on simple mechanical dynamics by using reasonable assumptions in AC motor control systems. However, these simple approaches give undesired performances under mismatches of model parameters, external load conditions and unknown dynamics. Moreover, in real systems as well as during analysis, speed estimation error is inevitable and should be considered from the viewpoint of the stability of the whole feedback control system. To cope with the robust characteristics under inherent uncertainties, a nonlinear uncertainty observer using the radial basis function networks (RBFNs) is proposed. A control law for stabilising the system and adaptive laws for updating both weights in the RBFNs and a bounding constant are established, so that the whole closed-loop system is stable in the sense of Lyapunov. Additionally, stability proof of the whole control system including the speed estimation error is presented. The proposed approach is applied to a three-level inverter-fed induction motor direct torque control (DTC) system, and computer simulations as well as experimental results are presented to show the validity and effectiveness of the proposed system.