Distributed Adaptive Finite-iteration Iterative Learning Control for High-speed Trains

This study explores the distributed finite-iteration adaptive iterative learning tracking control problem of high-speed trains. Firstly, by comprehensively considering various system resistances and coupling forces between carriages, a distributed dynamic model of high-speed trains is established. Subsequently, aiming at the limitations of existing infinite-iteration iterative learning control in practical high-speed train operation systems, a distributed adaptive finite-iteration iterative learning control method is proposed. This method designs a new type of controller and an adaptive learning control law, and utilizes the composite energy function technique to ensure that the train speed tracking error not only converges to zero as the number of iterations approaches infinity, but also can reach any desired tracking accuracy after a finite number of iterations. Finally, numerical simulations verify that with the increase in the number of runs, the tracking performance of the train to the reference trajectory is significantly improved, and it can reach any desired tracking accuracy after a finite number of iterations.