Finite-Iteration Constrained Robust Adaptive Iterative Learning Control for High-Speed Train With Overspeed Protection and Tailgating Protection Under Disturbances

To ensure perfect tracking performance, most of the existing iterative learning control (ILC) methods applied to automatic high-speed train (HST) operation control systems require that the number of operations/iterations approaches infinity. It is not realistic in the actual practice of automatic train operation control. In this paper, a new finite-iteration constrained robust adaptive iterative learning control (FICRAILC) method is proposed. The proposed method can not only achieve finite-iteration convergence but also guarantee both overspeed protection and tailgating protection to ensure safety. Specifically, the number of iterations required for convergence can be obtained directly via calculation and can be flexibly adjusted according to the desired tracking control accuracy, adjustable controller parameters and initial system values. Moreover, a new control strategy that integrates the distance information of neighboring trains into the designed controller is constructed, which can adjust the distance with neighboring trains to always maintain a safe distance from neighboring trains. In addition, various uncertainties in the system are approximated and compensated by designing an iterative learning radial basis function neural network (RBFNN). To verify the availability of the proposed FICRAILC method further, simulations of a train operation control system similar to Chinese Railway HST is provided.