Intelligent Traction Control Method Based on Model Predictive Fuzzy PID Control and Online Optimization for Permanent Magnetic Maglev Trains
Yahui Liu, Kuangang Fan, Qinghua Ouyang
Abstract
Considering that the speed control system of the suspended permanent magnetic maglev train is more complicated and the parameters are more unstable than those of other trains, the traditional speed-tracking algorithm has large tracking errors, frequent controller output changes, high energy consumption, and decreasing the passengers' riding comfort. To improve the shortcomings of the traditional automatic train operation (ATO) control algorithm, this paper proposes a predictive fuzzy proportional-integral-derivative control algorithm with weights (WM-F-PID). The main contribution of this work is to propose a cascaded predictive fuzzy PID (F-PID) control algorithm architecture with weights and use an improved steepest descent method to calculate online the weight of the F-PID controller input occupied by the predictive controller output. Compared with the proportional-integral-derivative (PID), F-PID, model predictive control (MPC), and simple cascade predictive fuzzy PID (M-F-PID) control algorithms, this control algorithm effectively improves train tracking accuracy and comfort and reduces train energy consumption and stopping errors.