Quasilinear Fuzzy Polytopic Predictive Control for the Traction Converters in the High-Speed Train

This paper proposed quasilinear fuzzy polytopic predictive control (QFPC) using a two-tiered iterative optimization procedure to suppress harmonics and low-frequency oscillation caused by parameter uncertainties in high-speed trains. The proposed dynamic output-feedback controller combines the benefits of polytopic linear parameter varying systems, conventional model predictive control, and the Sugeno/Takagi fuzzy model to create a fuzzy product of functions. The unknown disturbance and parameter fluctuations that make up the lumped disturbance are contained inside a convex and compact polytope. Then, a QFPC unit for achieving asymptotic stability in polytopic uncertain systems is designed. Moreover, a comprehensive functional representation of several fuzzy inference subsystems is developed to reduce the difference in prediction inaccuracies between a specified reference path and targeted output. The two-layered computing technique eliminates the need for intensive online nonlinear optimization. It makes constructing a controller using linear control theory possible while reducing the time spent on the calculation to a minimum. The proposed QFPC suppressed the effects of the oscillations. In the end, a step-by-step implementation procedure is provided. Experimental validation is used to analyze the advantages of the suggested approach. Furthermore, the proposed method is straightforward, and the implementation is easy.