On the effectiveness of Stable Model Predictive vs. Adaptive Fuzzy Sliding Mode Method in synthesizing the controller for High-Speed Trains

In this paper, two different control approaches are developed to control a high-speed train (HST) with particular emphasis on velocity and position tracking, which is essential for maintaining the safe distance between every two wagons. To demonstrate whether non-model-based methods better control high-speed trains or model-based ones, an adaptive fuzzy sliding mode controller (AFSMC) and a constrained stable model predictive controller (SMPC) are proposed. The SMPC is implemented on a linearized train's model considering safe distance as a constraint. On the other hand, an AFSMC controller is implemented on the nonlinear model, which is more accurate. Both proposed controllers can control the high-speed train perfectly. The effectiveness of the proposed control algorithms is authenticated by theoretical analysis and numerical simulations. Results illustrate that SMPC can control high-speed trains more precisely.