Research on Fuzzy Adaptive Model Predictive Control Methodology for Heavy-Haul Trains - YI Jie; JIANG Fan; LUO Yuan; ZHANG Zhengfang; BAI Jinlei; ZHONG Puhua (Kongzhi Yu Xinxi Jishu)
The locomotive autonomous operation system is characterized by its inherent nonlinear and time-varying nature. Operating trains encounter a diverse range of track conditions, which necessitates not only predictive control but also the dynamic adjustment of controller parameters to adapt to these varying conditions. To address these challenges, this paper presents a fuzzy adaptive model predictive control (FAMPC) approach specifically for heavy-haul trains. Based on the establishment of a nonlinear multi-mass model for heavy-haul trains, a model predictive control system framework was constructed, to incorporate optimization processes that consider multiple objectives and operational constraints. This model aims to predict and dynamically adjust the operational states of trains. To mitigate the inadaptability associated with fixed controller parameters in response to changing track conditions, this paper integrates fuzzy control with model predictive control methods, to facilitate the adaptive optimization control for trains running on different railways, by enabling the adaptive tuning of weight coefficients within their model predictive controllers. Simulation results revealed the good control performance of the integrated controller configuration, highlighting its ability to ensure the stable train operation while achieving accurate train control within speed errors of ±1 km/h.