Guided deep reinforcement learning framework using automated curriculum scheme for accurate motion planning DOI
Deun-Sol Cho, Jae-Min Cho, Won-Tae Kim

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 139, P. 109541 - 109541

Published: Nov. 11, 2024

Language: Английский

An immune optimization deep reinforcement learning control method used for magnetorheological elastomer vibration absorber DOI

Chi Wang,

Weiheng Cheng, Hongli Zhang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 137, P. 109108 - 109108

Published: Aug. 8, 2024

Language: Английский

Citations

5
A data-driven MPC approach for virtually coupled train set with non-analytic safety distance DOI
Xiaolin Luo, Dongming Wang, Tao Tang

et al.

Transportation Research Part C Emerging Technologies, Journal Year: 2025, Volume and Issue: 174, P. 105087 - 105087

Published: March 15, 2025

Language: Английский

Citations

0
A Cooperative Control Based Adaptive Zone Method For Virtually Coupled Train Set DOI
S. Che,

Debiao Lu,

Baigen Cai

et al.

International Journal of Fuzzy Systems, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Language: Английский

Citations

0
Multi-step look ahead deep reinforcement learning approach for automatic train regulation of urban rail transit lines with energy-saving DOI
Yunfeng Zhang, Shukai Li, Yin Yuan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 145, P. 110181 - 110181

Published: Feb. 6, 2025

Language: Английский

Citations

0
Safety Prescribed Performance Control of Virtually-Coupled Trains with Multifold Kinematic Targets and Switching Constraints DOI
Wenxiao Si, Shigen Gao

ISA Transactions, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Virtual track irregularity establishment for load prediction of heterogeneous bogie frames for virtually coupled trainsets DOI

Chengxiang Ji,

Youwei Song,

Sun Shou-guang

et al.

Vehicle System Dynamics, Journal Year: 2024, Volume and Issue: 63(1), P. 163 - 188

Published: Aug. 26, 2024

In this paper, we establish the virtual track irregularity (VTI) considering suspension system dynamics for load prediction of heterogeneous bogie frames coupled train sets (VCTS). At first, dynamic characteristics axlebox spring set and primary vertical damper A/B-type high-speed trains are analyzed compared establishment frequency-varying parameter simplified vehicle models (FVP-SVDMs). Then, a frequency-segmented time-domain inversion (FSTIM) VTI suitable FVP-SVDMs is conducted, quantitative evaluation method consistency established. Finally, VTIs based on measured loads quantitatively assessed, agreement between predicted spectra evaluated. Based FVP-SVDM corresponding VTI, equivalent amplitude error bouncing 7%. The technology provides possibility VCTS to eliminate need conducting main test before applying new vehicles line. addition, fleet running same through communication, will provide theoretical support implementing active control across entire fleet.

Language: Английский

Citations

1
Guided deep reinforcement learning framework using automated curriculum scheme for accurate motion planning DOI
Deun-Sol Cho, Jae-Min Cho, Won-Tae Kim

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 139, P. 109541 - 109541

Published: Nov. 11, 2024

Language: Английский

Citations

0