Citation: | CHEN Ping, SHI Tiancheng, YU Mingyue, SHAN Lei. Self-Learning Model Reference Adaptive Levitation Control Strategy[J]. Journal of Southwest Jiaotong University, 2023, 58(4): 799-807. doi: 10.3969/j.issn.0258-2724.20220752 |
A self-learning model reference adaptive control strategy was proposed to solve the problems of unknown nonlinear force and uncertain transfer function of levitation controllers, which were caused by track irregularity in electromagnetic levitation trains. The tunable parameters in the control algorithm were adjusted according to the system state, error, and time, so as to make the gap stabilize at a constant value. In order to avoid slow adjustment of tunable parameters, the learning rate was dynamically adjusted according to the error of target gaps, so as to guarantee that the gap fluctuation was smaller during stable levitation. The stability of the model reference adaptive control system was confirmed by a Lyapunov framework, and the proposed control strategy was simulated by MATLAB/Simulink. The results show that the root-mean-square error (RMSE) of the gap of the self-learning model reference adaptive control algorithm is 0.12, and setting appropriate initial values of tunable parameters and limiting their amplitude can improve the robustness of the controller. When the algorithm is tested on a single levitation frame, the acceleration signal is obtained by the controller. The rising and adjustment time of the proposed algorithm is 1.21 s and 2.04 s, respectively. It proves that the learning rate of the method can be adjusted dynamically, which improves the adaptive ability of the controller.
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