Iterative Learning based Model Free Adaptive Control for Subway Trains with Speed and Input Constraints

In this paper, a partial form dynamic linearization based model free adaptive control (PFDL-MFAC) with iterative learning control (ILC) method is proposed for subway train speed tracking control subject to speed and input constraints by only using input output data of the subway train system. Firstly, under the speed and input constraints, a double loop controller structure is designed, which consists of ILC feedforward control loop and MFAC feedback control loop. It is a modular design method to realize the complementary advantages of MFAC and ILC. Secondly, for the influence of the time-iteration-dependent measurement disturbances on the subway train system, a robust controller design is also investigated. Furthermore, the speed and input constraints are never violated. Finally, the effectiveness of the designed control scheme is verified by a simulation of the subway train system.