Feedback Linearization Adaptive Dynamic Sliding Mode Control of Linear Synchronous Motor for CNC Machine Tools

Zhang, Kang; Wang, Limei; Fang, Xin

doi:10.1007/s42835-021-00930-2

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…In order to verify the stability and effectiveness of the controller designed in this paper, the Lyapunov function is used for stability proof (Zhang & Wang, 2021). Combined with the cascade error and adaptive parameters, the Lyapunov function shown below is designed…”

Section: Controller Designmentioning

confidence: 99%

High-speed train cooperative control based on fractional-order sliding mode adaptive algorithm

Lin

Liang

2023

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PurposeThis study aims to propose an adaptive fractional-order sliding mode controller to solve the problem of train speed tracking control and position interval control under disturbance environment in moving block system, so as to improve the tracking efficiency and collision avoidance performance.Design/methodology/approachThe mathematical model of information interaction between trains is established based on algebraic graph theory, so that the train can obtain the state information of adjacent trains, and then realize the distributed cooperative control of each train. In the controller design, the sliding mode control and fractional calculus are combined to avoid the discontinuous switching phenomenon, so as to suppress the chattering of sliding mode control, and a parameter adaptive law is constructed to approximate the time-varying operating resistance coefficient.FindingsThe simulation results show that compared with proportional integral derivative (PID) control and ordinary sliding mode control, the control accuracy of the proposed algorithm in terms of speed is, respectively, improved by 25% and 75%. The error frequency and fluctuation range of the proposed algorithm are reduced in the position error control, the error value tends to 0, and the operation trend tends to be consistent. Therefore, the control method can improve the control accuracy of the system and prove that it has strong immunity.Originality/valueThe algorithm can reduce the influence of external interference in the actual operating environment, realize efficient and stable tracking of trains, and ensure the safety of train control.

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Section: Controller Designmentioning

confidence: 99%