Constrained Adaptive Finite-iteration Learning Fault-tolerant Control for High-speed Train
s:This paper focuses on the speed control of high-speed train(HST)automatic operation system under actuator fault and speed limitation,and proposes a Finite-Iteration Constrained Adaptive Iterative Learning Fault-Tolerant Control(FI-CAILFTC)method.Based on the Barrier Composite Energy Function(BCEF),this paper defines the convergence conditions for a finite number of operations along the iterative domain,and calculates the required number of operations using the desired arbitrary tracking accuracy.The method then improves the controller parameter selection to ensure finite number of operation convergence.The iterative learning control algorithms are designed with adaptive fault tolerance for adaptive estimation and compensation of unknown time-varying and iteration-varying actuator faults.To address the overspeed issue of the HST operation,this study added an overspeed protection mechanism to ensure that the actual operation speed of the HST always meets the speed constraints and to ensure the safe operation of the train.The China Railway High-speed(CRH)-3 high-speed locomotive train is used as an example for the simulation analysis.The results show that the HST speed tracking error under the FI-CAILFTC method reaches the desired control accuracy of 0.2 after the pre-calculated 17th iteration,compared with the comparison algorithms,the control accuracy was improved by 90.70%and 90.22%,respectively.The FI-CAILFTC has faster convergence and better adaptive fault tolerance.The actual operation speed of the HST is always active to satisfy the speed constraints.
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