The speed tracking control system of maglev trains has the characteristics of complexity, instability, and multiple disturbances. The traditional speed tracking control algorithm has low tracking accuracy, large tracking errors and easy external interference, so it will increase train energy consumption and reduce passengers’ riding comfort. In order to improve the deficiencies in the existing control algorithms, the disturbance rejection PID (DR-PID) algorithm based on fuzzy control (F-DR-PID) is proposed. The DR-PID controller increases the anti-disturbance capability and tracking accuracy of the control system, and the fuzzy control algorithm adjusts the three weights of the DR-PID in real time. To verify the effectiveness of the F-DR-PID control algorithm, this paper is compared and analyzed with PID, F-PID and DR-PID control algorithms under the same experimental conditions. The results show that the F-DR-PID control algorithm has better tracking accuracy and passengers’ riding comfort and reduces train energy consumption and parking error.
This paper focuses on speed tracking control of the maglev train operation system. Given the complexity and instability of the maglev train operation system, traditional speed-tracking control algorithms demonstrate poor tracking accuracy and large tracking errors. The maglev train is easily affected by external interference, increasing train energy consumption and reducing passengers’ riding comfort. This study proposes a control algorithm called APSO-NLADRC to address the deficiencies of the automatic train operation control algorithms. The APSO-NLADRC is based on adaptive particle swarm optimization (APSO) algorithm parameter optimization nonlinear active disturbance rejection controller (NLADRC). The method of population comparison, linear update of learning factors, and adaptive updating of inertia weight values addresses the premature convergence phenomenon that occurs during the parameter optimization of the traditional particle swarm algorithm. The APSO algorithm solves the problem that the parameters of the NLADRC are difficult to adjust. Compared with PID, NLADRC, and NLADRC based on traditional particle swarm optimization algorithms, the proposed control algorithm has higher tracking accuracy and more robust anti-interference capability and provides better comfort.
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