Speed tracking control of high-speed train based on adaptive control and linear active disturbance rejection control

Abstract

This paper proposes a hybrid control scheme combining adaptive control (APC) and linear active disturbance rejection control (LADRC) to solve the high-speed train (HST) speed tracking control problem. First, in order to meet the actual operation of HST, a multi-mass point dynamic model with time-varying coefficients is established. Second, LADRC is proposed to control the speed of the train, and the anti-disturbance ability of the system is improved by estimating and compensating the total disturbance suffered by the carriage. Third, in order to compensate for the observation error caused by the bandwidth of the linear extended state observer, APC is introduced. The adaptive laws are set for LADRC and train time-varying coefficients, which can effectively eliminate the adverse effects of time-varying coefficients on train operation and improve the control performance of the controller. Meanwhile, the Lyapunov theory is used to prove the stability of the system. Finally, the simulation results show that the hybrid control is more effective in solving the problem of HST speed tracking.

Harmonics suppression in high-speed railway via single-phase traction converter with an LCL filter using fuzzy logic control strategy

“…These results underscore the notable effectiveness of the FLC in reducing harmonics in comparison to the PI controller, suggesting that the adoption of the FLC could constitute a significant improvement in LCL rectifier applications, especially when it is crucial to effectively suppress highorder harmonic resonance. Figures 13,14 depict simulated waveforms of the gridside current I g of the LCL-type converter with PI and FLCs, respectively. The ability to eliminate high-frequency harmonics proves to be a crucial factor in the evaluation of filters and control systems.…”

“…However, the process of adjusting the PI parameters in the voltage or current control loop can be a problem, as incorrect settings may result in undesired low-frequency oscillations in traction networks. In order to overcome this issue, various advanced nonlinear control techniques like model predictive control [14] and sliding mode control [15] were proposed in order to improve the dynamic behavior of systems. Nonetheless, implementing and adjusting parameters for these controllers in real-world scenarios can be difficult due to the presence of unknown parameters, complex structures and intricate mathematical models.…”