Finite Control Set Model Predictive Control of Six-Phase Induction Motor

Abstract

Model predictive control (MPC) is a closed-loop technique to generate inputs to activate the process so that the outputs of the interest track a certain set point or reference signal. MPC performs this in a closed loop by updating the decision on the value of the input variables based on measurements from the system. MPC uses an explicit process model to predict what the system will do if a specific sequence of inputs is applied in the future. The MPC methods are popular in electrification and ac drive systems due to fast dynamic response. It offers great flexibility in handling multiple control goals and nonlinearities. This paper proposes a novel Finite Control Set MPC method that aims to keep control transitions to a minimum. In the proposed method, it is desired to achieve control targets by using minimum energy. The designed MPC controller meets all requirements for six-phase motor control. The proposed mathematical concept has been proven by experimental results. The experimental results have shown that the proposed method regulates system dynamics in a stable and effective way.