Adaptive Fuzzy Iterative Learning Control for High-Speed Trains With Both Randomly Varying Operation Lengths and System Constraints

In this article, a new adaptive fuzzy iterative learning control (AFILC) method is proposed for the tracking control of nonlinear uncertain high-speed train (HST) operation systems that have both randomly varying iteration lengths and speed and input force constraints. To cope with unknown time-varying basic resistance coefficients, an adaptive learning control law and two fully projected parameter learning laws are designed. The nonparametric and unknown additional resistance in the HST operation system is compensated and integrated into the control law by means of a newly constructed adaptive iterative learning fuzzy system. Moreover, due to the complex operation environment and various uncertainties, disturbances and emergencies, trains are often early or late compared with the prearranged timetable rather than being strictly on time in the repeated operations of each day, which leads to randomly varying operation lengths for actual HST running. Furthermore, as the traveling speed of modern HSTs increases, both the train's speed and input force should be constrained to guarantee the safe operation. Fortunately, the proposed AFILC can not only actively manipulate the position, speed, and input force of the train into prespecified and constrained ranges for safe operation but can also make both the position and speed tracking control errors converge to zero over the whole desired and scheduled time interval, even if the actual time interval varies in each operation of the HST. Simulations on a practical train operation system similar to China Railway High-speed (CRH)-3 train are further presented to demonstrate the applicability and effectiveness of the proposed method.