A Dynamic Linearized Optimal Iterative Learning Control with Constraints and Its Application to High-Speed Trains

This paper considers the constraint problem of input constraint and controller parameter for nonlinear repetitive high-speed trains. To address this problem, a data-driven optimal iterative learning control method is proposed based on controller dynamic linearization. In particular, the constrained optimization problem is transformed into an unconstrained optimization problem by introducing the Lagrange method of linear quadratic convex form. Then, the adaptive learning estimation of the controller parameter is obtained through the regularized least square method with analytic suboptimal solution. Finally, the simulations on a numerical example and high-speed train demonstrate that the proposed method has good convergence and tracking accuracy while satisfying constraints.