Data-Driven Sliding Mode Optimal Control for Multi-Power Unit High-Speed Train: A Composite Approach

In this study, a multiple-input multiple-output (MIMO) data-driven sliding mode optimal control scheme is investigated for a multi-power unit high-speed train (HST) automatic driving system under disturbances. First, an integral terminal sliding mode control (ITSMC) law based on the dynamic linearization method is introduced to achieve error convergence in finite time. Subsequently, a parameter update law and an adaptive extended state observer (AESO) are designed to estimate the control gain and total uncertainty, respectively, which solves the problem of traditional sliding mode control requiring a large switching gain to handle the disturbance. Third, an optimal control signal is obtained by predictive control to achieve a higher level of tracking error accuracy and quickly reach the quasi-sliding mode state, and the compound optimal control scheme is derived under the combined action of the ITSMC and predictive control. The scheme considers and compensates for the total uncertainty caused by the error feedback, parameter estimation error, and unknown nonlinearity. The main advantages of this scheme include the following: controller design process uses only system data, provides excellent model adaptability, and disturbance rejection ability. Upon providing a stability-proof analysis of the proposed method, the proposed composite control method was compared and tested on a CRH380A train simulation test bench equipped in the laboratory. The simulation results show that the speed tracking errors of each power unit of the HST under the proposed control scheme are within [<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula>0.123 km<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>h<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula>, 0.144 km<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>h<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula>]. The control forces and accelerations are within [<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula>55 kN, 46 kN] and [<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula>0.592 m<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>s<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-2}$</tex-math></inline-formula>, 0.521 m<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>s<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-2}$</tex-math></inline-formula>] respectively, which meets the requirements of safe, stable, and efficient operation of the train.

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Publication Details

Journal
IEEE Transactions on Intelligent Vehicles
Published
2024-10-10
DOI
https://doi.org/10.1109/tiv.2024.3478275
Citations
3
Primary Topic
Railway Systems and Energy Efficiency
Type
article
Field-Weighted Citation Impact
1.05
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article

Data-Driven Sliding Mode Optimal Control for Multi-Power Unit High-Speed Train: A Composite Approach

Hui Yang, Zhen Sui, Chang Tan, Liang Zhou et al.
3 citations
IEEE Transactions on Intelligent Vehicles
Railway Systems and Energy Efficiency
1.05
article

Data-Driven Sliding Mode Optimal Control for Multi-Power Unit High-Speed Train: A Composite Approach

Hui Yang, Zhen Sui, Chang Tan, Liang Zhou, Yating Fu, Zhongqi Li
article en
3 citations

Abstract

In this study, a multiple-input multiple-output (MIMO) data-driven sliding mode optimal control scheme is investigated for a multi-power unit high-speed train (HST) automatic driving system under disturbances. First, an integral terminal sliding mode control (ITSMC) law based on the dynamic linearization method is introduced to achieve error convergence in finite time. Subsequently, a parameter update law and an adaptive extended state observer (AESO) are designed to estimate the control gain and total uncertainty, respectively, which solves the problem of traditional sliding mode control requiring a large switching gain to handle the disturbance. Third, an optimal control signal is obtained by predictive control to achieve a higher level of tracking error accuracy and quickly reach the quasi-sliding mode state, and the compound optimal control scheme is derived under the combined action of the ITSMC and predictive control. The scheme considers and compensates for the total uncertainty caused by the error feedback, parameter estimation error, and unknown nonlinearity. The main advantages of this scheme include the following: controller design process uses only system data, provides excellent model adaptability, and disturbance rejection ability. Upon providing a stability-proof analysis of the proposed method, the proposed composite control method was compared and tested on a CRH380A train simulation test bench equipped in the laboratory. The simulation results show that the speed tracking errors of each power unit of the HST under the proposed control scheme are within [<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula>0.123 km<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>h<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula>, 0.144 km<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>h<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula>]. The control forces and accelerations are within [<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula>55 kN, 46 kN] and [<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula>0.592 m<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>s<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-2}$</tex-math></inline-formula>, 0.521 m<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>s<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-2}$</tex-math></inline-formula>] respectively, which meets the requirements of safe, stable, and efficient operation of the train.

IEEE Transactions on Intelligent VehiclesVol. 10(8)
East China Jiaotong University (CN), Jilin University (CN), Jilin Medical University (CN)
Affordable and clean energy
Openalex Percentile: Top 20%
Railway Systems and Energy Efficiency
1.05
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