Research On Sliding Mode Control For AC Motor Drives

AC motor is a high order and strong coupling system, and generally it is difficult to describe the accurate mathematic model of an AC motor with some external disturbances, so applicable control strategies are expected to ensure good performance of motor control. In this thesis, we consider using sliding model control in motor control, in order to solve the stator current coupling problem and improve the robustness of overall system.This thesis studies the three phase asynchronous motor, analyzes the stator current coupling effectiveness, and summarizes control methods proposed by scholars all over the world. However, these proposed methods have some drawbacks, such as bad robustness. Therefore, sliding mode control is considered in this thesis.The thesis expounds the basic theory of sliding mode control, and proposes a novel control strategy. The most significance of this thesis lies in proposing a new sliding mode control method, which can reduce the chattering caused by sliding mode effectively. Although the traditional sliding mode control has strong robustness and it is easy to implement in engineering, sliding mode control has a drawback-the chattering. Neural networks have the ability to approximate nonlinear functions. In addition, radial basis function neural network (RBFNN) has an advantage of faster learning ability and less chance of falling into local minimum, in comparison with standard BP neural network. Therefore, this thesis considers combining the merits between sliding mode control and neural network. Unlike conventional SMC, in this proposed control method a RBFNN controller replaces the output of the sliding mode controller to eliminate undesired chattering.In addition, this thesis uses this method in speed control and conducts simulation experiments.