Model‐free adaptive and iterative learning composite control for subway train under actuator faults
Qian Wang, Shangtai Jin, Zhongsheng Hou, Guangzhuo Gao
Abstract
Abstract In this article, the model‐free adaptive and iterative learning composite control (CMFAC‐ILC) is proposed to ensure the speed and position tracking for the subway train system subjected to the iteration‐time‐varying actuator faults. First, a nonlinear subway train system is transformed into a faults‐related full‐form dynamic linearization data model (FFDLDM), which relies on the input, output, and faults data of the subway train system. The actuator faults and unknown nonlinear terms of the subway train system are estimated by the projection algorithm. Then, in the time domain, the model‐free adaptive control (MFAC) algorithm is utilized and unknown controller parameters are estimated by the RBFNN algorithm. In the iteration domain, a feedforward D‐type iterative learning control (ILC) algorithm is added to the outer loop of the MFAC algorithm. Finally, the theoretical analysis proves that the speed and position tracking errors of the subway train are bounded, the simulations demonstrate the effectiveness of the proposed composite control scheme of the subway train.