Fuzzy adaptive control particle swarm optimization based on T-S fuzzy model of maglev vehicle suspension system

Abstract

At present, with the gradual promotion of Maglev vehicles, the stability of the suspension system has gradually become a hotspot. During the operation of Maglev vehicles, vibration may be caused by external disturbances such as track irregularity, non-directional wind load and load variation. When the vibration amplitude is within the controllable range of the current parameters, the restraint effect can be achieved and the stable convergence can be formed. However, when the vibration amplitude exceeds the current controllable range, the maglev vehicle may break the track or even lose stability. In order to solve the possible adverse effects of external disturbances on the stability of the system, a T-S fuzzy model considering both parameter uncertainties and external disturbances is constructed, and a relatively mature fuzzy adaptive control method is used for suspension control. However, considering the tracking performance of the system control parameters and the response speed of the parameter changes when the external disturbance changes, the particle swarm optimization (PSO) algorithm is used to optimize the system. The effectiveness of the optimized fuzzy adaptive control law in coordinating the closed-loop stability of the suspension system is proved in terms of response speed and convergence performance. Based on linear matrix inequality (LMI), the control response region satisfying the control performance after optimization is defined, and Lyapunov method is adopted to prove the stability of the optimized algorithm in controlling vehicle fluctuation operation. The simulation and experimental results show that the fuzzy adaptive control algorithm optimized by particle swarm optimization can further improve the speed of parameter optimization and the tracking performance of the system in the face of external disturbances and internal system parameter perturbations within a given range of control parameters. Compared with previous control strategies, the controller can greatly improve the response speed and the closed-loop information updating ability of the system in the face of disturbances, so that the system has stronger robustness and faster dynamic response.