Model Predictive Thrust Control for Linear Induction Machine: A Fuzzy Optimization Approach

Model predictive thrust control (MPTC) is one of the most effective approaches for linear induction machine (LIM) drive system. It can achieve the optimization of multiple objectives with suitable weighting factors, such as low ripples and fast dynamic performance. Due to the longitudinal end effect, the selection of weighting factor becomes particularly important in LIM. However, the process of tuning the weighting factors in the objective function is very long and tedious. In this article, a dynamic fuzzy MPTC approach is proposed to solve the multi-objective problem for minimizing the flux and thrust ripples. Based on the fuzzy optimization technique, the tuning process of weighting factors can be transformed into a discrete membership function. A max-min objective function of voltage vectors is employed to select a more balanced switch combination on the Pareto optimal-frontier for LIM. Furthermore, this approach can adapt to different operating conditions by adjusting objective priority ofmembership function. At last, comprehensive simulation and experimental results are conducted to demonstrate the effectiveness and feasibility of the proposed method.

Automated summary

Model predictive thrust control (MPTC) is an effective approach for linear induction machine (LIM) drive system, achieving optimization of multiple objectives with suitable weighting factors. This article proposes a dynamic fuzzy MPTC approach to solve the multi-objective problem of minimizing flux and thrust ripples. By using fuzzy optimization technique, the tuning process of weighting factors is transformed into a discrete membership function. A max-min objective function of voltage vectors is employed to select a more balanced switch combination on the Pareto optimal-frontier for LIM. The approach is also adaptable to different operating conditions by adjusting objective priority of membership function. Comprehensive simulation and experimental results demonstrate the effectiveness and feasibility of the proposed method.