Passivity-Based Model Predictive Control of Three-Level Inverter-Fed Induction Motor

Abstract

Finite control set model predictive control (FCS-MPC) of three-level neutral point clamped inverter-fed induction motor has received wide concern recently, thanks to its low switching frequency and fast dynamics. However, the performance largely depends on the accurate measurement and parameters which determine the feedback and control loops, respectively. Besides, FCS-MPC demands a large number of resources to perform the exhaustive search, which indicates that the computation burden increases exponentially with the increasing switching states. Aiming at improving the robustness under the condition of unavoidable measuring noises and parameter variation as well as reducing the computational burden, a passivity-based model predictive control (PB-MPC) scheme is presented in this article. For an ideal system, the PB-MPC has a similar character as that of the FCS-MPC. In real applications, where noises and disturbances impact the system more or less, PB-MPC outperforms FCS-MPC due to the power shaping and damping injection inherited from passivity-based control which ensures the asymptotic stability. A Lyapunov function is designed to prove the stability of the proposed scheme. The stability and efficiency are verified by both simulation and experimental results.