Train speed tracking control based on adaptive decomposition fuzzy system

To address the speed tracking problem of trains affected by time-varying speed delays, model uncertainties, and random noise disturbances, this paper proposes a train speed tracking scheme based on the Adaptive Decomposed Fuzzy System (ADFS). The Lyapunov function method is employed to prove the stability of the proposed controller. Simulation comparisons are conducted with the Adaptive Fuzzy System (AFS) and a conventional sliding mode controller (SMC). The results show that the proposed ADFS achieves higher tracking accuracy, smaller mileage tracking error, smoother traction/braking force, and better real-time performance. In addition, the proposed method does not require an accurate train model or pre-designed fuzzy rules, and its parameters can be adjusted online through adaptive laws. Therefore, the proposed ADFS demonstrates advantages in robustness, practicality, and control efficiency for train speed tracking under complex operating conditions.