Sliding mode robust control based on HJI theory and RBFNN for high-speed train

Yating Fu; Dongliang Hu; Hui Yang; Tianhua Zhan

doi:10.1117/12.2639394

27 June 2022 Sliding mode robust control based on HJI theory and RBFNN for high-speed train

Yating Fu, Dongliang Hu, Hui Yang, Tianhua Zhan

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 122530M (2022) https://doi.org/10.1117/12.2639394
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

In view of the complex operating environment of high-speed trains, this paper considers the additional resistance of complex lines and the influence of random external disturbances. Aiming at this problem, a sliding mode robust control algorithm for high-speed trains is proposed based on Hamilton-Jacobi Inequality (HJI) theory and radial basis function neural network (RBFNN). On the other hand, the introduced RBFNN can be used to reduce the dependence of the controller on train model parameters, and HJI theory can be used to ensure the anti-jamming ability of the system. The Lyapunov function proves that both the displacement tracking error and the velocity tracking error can be converged and stable under the method proposed in this paper. The parameters of the CRH380A high-speed train are used for simulation, and the given target speed and target displacement curve are tracked to verify the feasibility of the proposed control algorithm. The simulation results show that the proposed control algorithm has better tracking accuracy for a given speed and displacement than the traditional robust adaptive control method (TRAC) and can meet the requirements of punctual operation and fixed-point parking required by high-speed trains. It has a better control effect when dealing with complex road conditions changes and also has the more vital anti-interference ability for random external interference.

Citation Download Citation

Yating Fu, Dongliang Hu, Hui Yang, and Tianhua Zhan "Sliding mode robust control based on HJI theory and RBFNN for high-speed train", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 122530M (27 June 2022); https://doi.org/10.1117/12.2639394

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KEYWORDS

Resistance

Detection and tracking algorithms

Algorithms

Control systems

Roads

Neural networks

Numerical simulations

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