Iterative learning control method for EMUs based on predictive control

LI Zhong-qi; ZHOU Liang; YANG Hui; YE Mei-han

doi:10.19818/j.cnki.1671-1637.2023.01.021

Volume 23 Issue 1

Feb. 2023

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Article Navigation > Journal of Traffic and Transportation Engineering > 2023 > 23(1): 280-290

LI Zhong-qi, ZHOU Liang, YANG Hui, YE Mei-han. Iterative learning control method for EMUs based on predictive control[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 280-290. doi: 10.19818/j.cnki.1671-1637.2023.01.021

Citation:

LI Zhong-qi, ZHOU Liang, YANG Hui, YE Mei-han. Iterative learning control method for EMUs based on predictive control[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 280-290. doi: 10.19818/j.cnki.1671-1637.2023.01.021

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Iterative learning control method for EMUs based on predictive control

doi: 10.19818/j.cnki.1671-1637.2023.01.021

LI Zhong-qi^{1, 2
,},
ZHOU Liang^{1, 2},
YANG Hui^{1, 2
,
,},
YE Mei-han^{1, 2}

1.
School Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China
2.
State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, Jiangxi, China

Funds:

National Natural Science Foundation of China 52162048

National Natural Science Foundation of China 61991404

National Natural Science Foundation of China 62003138

National Key Research and Development Program of China 2020YFB1713703

Jiangxi Provincial Program for Academic and Technical Leaders Training of Major Disciplines 20213BCJ22002

More Information

Author Bio:
LI Zhong-qi(1975-), male, professor, PhD, lzq0828@163.com

YANG Hui(1965-), male, professor, PhD, yhshuo@263.net
Received Date: 2022-08-28
Available Online: 2023-03-08
Publish Date: 2023-02-25

Abstract

Abstract

Addressing the issues of nonlinear disturbance and time-varying parameters in the operation of electric multiple units (EMUs), an iterative learning control method for EMUs based on the predictive control was proposed to improve the speed tracking accuracy and passenger comfort requirements of EMUs. By collecting the input and output data from the previous operation of the EMUs, the least square method with a forgetting factor was used to identify the parameters of the predictive model in the generalized predictive control (GPC) in real time and calculate the predicted output. The predicted output was corrected based on the average model error from the previous process, and the iterative learning control law was derived from the corrected predicted output. The new control quantity was calculated in real time through the online calculation to realize the speed tracking of the EMUs. The modified predictive output was adopted to design the quadratic iterative learning control law. The problems of difficult parameter tuning, slow convergence speed and poor robustness of the traditional proportional integral differential (PID) iterative learning were solved by fully learning the repetitive characteristics of the train system. In addition, the convergence proof of the algorithm was provided. The proposed method was tested using the semi-physical simulation platform of CRH380A EMUs equipped in the laboratory. A three-power unit model of the train was established to track the set speed curve, and a comparison was made with some traditional algorithms. Simulation results show that in the eighth iteration process, the iterative learning control method for high-speed EMUs based on the predictive control achieves the power unit speed and acceleration errors within 0.3 km·h^-1 and 0.5 m·s^-2, respectively, and the changes are stable. Its performance is better than PID, GPC and proportional iterative learning control (P-ILC), and meets the requirements of train tracking accuracy and passenger comfort. In the case of sudden changes in model parameters, the proposed method can enable the train to timely correct the uncertainties caused by the model mismatch, time-variation and interference.
- electric multiple units,
- train speed tracking,
- generalized predictive control,
- iterative learning control,
- multi-power unit,
- intelligent train driving

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References(31)

References

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Iterative learning control method for EMUs based on predictive control

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