Predictive Sliding Mode Control for High-Speed Trains via Adaptive Extended State Observer Under Input Constraints: A Model-Free Scheme

A novel MIMO data-driven integral predictive sliding mode control (DIPSMC) scheme is proposed based on the dynamic linearization (DL) and state observer method, intended for the automatic driving systems of multi-power unit high-speed trains (HSTs) influenced by system couplings, input constraints, and external disturbances. Initially, by introducing a nonlinear fast integral terminal sliding mode (NFITSM) surface instead of the traditional sliding mode function, facilitating rapid convergence of system errors and reducing sliding mode chattering. Additionally, a parameter update law and an adaptive extended state observer (AESO) are designed to estimate input gains and total uncertainties, respectively, addressing the issue in traditional discrete-time sliding mode control (DSMC) that requires large switching gains to handle disturbances. Subsequently, combining the rolling time domain optimization concepts in predictive control, it follows the reference trajectory of the predefined reaching law, allowing the system to explicitly handle control constraints and obtain higher tracking accuracy. This scheme concurrently accounts for and compensates the total uncertainties arising from system couplings, parameter estimation errors, and unknown disturbances. The principal advantage of this scheme is its design based entirely on a DL data model equivalent to the HST system, characterized by a low-order controller with robust chattering mitigation and disturbance rejection capabilities. Finally, comparative testing experiments of the proposed scheme are conducted on the CRH380A HST simulation platform. Experimental results indicate that under the proposed control scheme, the velocity and displacement error ranges for each power unit of the HST are within ±0.156 km/h and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm 1.~1$ </tex-math></inline-formula> m, respectively, with control force and acceleration ranges of [−53.2 kN, 46.5 kN] and [−0.568 m/s<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, 0.476 m/s<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>], respectively, and low chattering levels, fulfilling the efficiency and safety requirements of the trains.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2025-08-14
DOI
https://doi.org/10.1109/tits.2025.3596543
Primary Topic
Railway Systems and Energy Efficiency
Type
article
Field-Weighted Citation Impact
0.00

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article

Predictive Sliding Mode Control for High-Speed Trains via Adaptive Extended State Observer Under Input Constraints: A Model-Free Scheme

Yuan Cao, Hui Yang, Chang Tan, Liang Zhou et al.
IEEE Transactions on Intelligent Transportation Systems
Railway Systems and Energy Efficiency
article

Predictive Sliding Mode Control for High-Speed Trains via Adaptive Extended State Observer Under Input Constraints: A Model-Free Scheme

Yuan Cao, Hui Yang, Chang Tan, Liang Zhou, Zhongqi Li, Y. Fu
article en

Abstract

A novel MIMO data-driven integral predictive sliding mode control (DIPSMC) scheme is proposed based on the dynamic linearization (DL) and state observer method, intended for the automatic driving systems of multi-power unit high-speed trains (HSTs) influenced by system couplings, input constraints, and external disturbances. Initially, by introducing a nonlinear fast integral terminal sliding mode (NFITSM) surface instead of the traditional sliding mode function, facilitating rapid convergence of system errors and reducing sliding mode chattering. Additionally, a parameter update law and an adaptive extended state observer (AESO) are designed to estimate input gains and total uncertainties, respectively, addressing the issue in traditional discrete-time sliding mode control (DSMC) that requires large switching gains to handle disturbances. Subsequently, combining the rolling time domain optimization concepts in predictive control, it follows the reference trajectory of the predefined reaching law, allowing the system to explicitly handle control constraints and obtain higher tracking accuracy. This scheme concurrently accounts for and compensates the total uncertainties arising from system couplings, parameter estimation errors, and unknown disturbances. The principal advantage of this scheme is its design based entirely on a DL data model equivalent to the HST system, characterized by a low-order controller with robust chattering mitigation and disturbance rejection capabilities. Finally, comparative testing experiments of the proposed scheme are conducted on the CRH380A HST simulation platform. Experimental results indicate that under the proposed control scheme, the velocity and displacement error ranges for each power unit of the HST are within ±0.156 km/h and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm 1.~1$ </tex-math></inline-formula> m, respectively, with control force and acceleration ranges of [−53.2 kN, 46.5 kN] and [−0.568 m/s<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, 0.476 m/s<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>], respectively, and low chattering levels, fulfilling the efficiency and safety requirements of the trains.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(11)
East China Jiaotong University (CN), Beijing Jiaotong University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangxi Province
Openalex Percentile: Top 69%
Railway Systems and Energy Efficiency
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