Active Steering Controller for Driven Independently Rotating Wheelset Vehicles Based on Deep Reinforcement Learning

This paper proposes an active steering controller for Driven Independently Rotating Wheelset (DIRW) vehicles based on deep reinforcement learning (DRL). For the two-axle railway vehicles equipped with Independently Rotating Wheelsets (IRWs), each wheel connected to a wheel-side motor, the Ape-X DDPG...

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Vydáno v:	Processes Ročník 11; číslo 9; s. 2677
Hlavní autoři:	Lu, Zhenggang, Wei, Juyao, Wang, Zehan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.09.2023
Témata:	Active control Algorithms Control algorithms Control theory Controllers Deep learning Design Digital twins Dynamic models Efficiency H-infinity control Machine learning Neural networks Product enhancement Railway tracks Reinforcement Robotics Robust control Rotation Scale models Steering Trains Vehicles Wheels Wheelsets
ISSN:	2227-9717, 2227-9717
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Abstract	This paper proposes an active steering controller for Driven Independently Rotating Wheelset (DIRW) vehicles based on deep reinforcement learning (DRL). For the two-axle railway vehicles equipped with Independently Rotating Wheelsets (IRWs), each wheel connected to a wheel-side motor, the Ape-X DDPG controller, an enhanced version of the Deep Deterministic Policy Gradient (DDPG) algorithm, is adopted. Incorporating Distributed Prioritized Experience Replay (DPER), Ape-X DDPG trains neural network function approximators to obtain a data-driven DIRW active steering controller. This controller is utilized to control the input torque of each wheel, aiming to improve the steering capability of IRWs. Simulation results indicate that compared to the existing model-based H∞ control algorithm and data-driven DDPG control algorithm, the Ape-X DDPG active steering controller demonstrates better curving steering performance and centering ability in straight tracks across different running conditions and significantly reduces wheel–rail wear. To validate the proposed algorithm’s efficacy in real vehicles, a 1:5 scale model of the DIRW vehicle and its digital twin dynamic model were designed and manufactured. The proposed control algorithm was deployed on the scale vehicle and subjected to active steering control experiments on a scaled track. The experimental results reveal that under the active steering control of the Ape-X DDPG controller, the steering performance of the DIRW scale model on both straight and curved tracks is significantly enhanced.
AbstractList	This paper proposes an active steering controller for Driven Independently Rotating Wheelset (DIRW) vehicles based on deep reinforcement learning (DRL). For the two-axle railway vehicles equipped with Independently Rotating Wheelsets (IRWs), each wheel connected to a wheel-side motor, the Ape-X DDPG controller, an enhanced version of the Deep Deterministic Policy Gradient (DDPG) algorithm, is adopted. Incorporating Distributed Prioritized Experience Replay (DPER), Ape-X DDPG trains neural network function approximators to obtain a data-driven DIRW active steering controller. This controller is utilized to control the input torque of each wheel, aiming to improve the steering capability of IRWs. Simulation results indicate that compared to the existing model-based H∞ control algorithm and data-driven DDPG control algorithm, the Ape-X DDPG active steering controller demonstrates better curving steering performance and centering ability in straight tracks across different running conditions and significantly reduces wheel–rail wear. To validate the proposed algorithm’s efficacy in real vehicles, a 1:5 scale model of the DIRW vehicle and its digital twin dynamic model were designed and manufactured. The proposed control algorithm was deployed on the scale vehicle and subjected to active steering control experiments on a scaled track. The experimental results reveal that under the active steering control of the Ape-X DDPG controller, the steering performance of the DIRW scale model on both straight and curved tracks is significantly enhanced.
Audience	Academic
Author	Lu, Zhenggang Wang, Zehan Wei, Juyao
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Cites_doi	10.1007/s40534-020-00207-w 10.1007/978-3-031-07305-2_10 10.3390/en16083490 10.1177/0954409718777374 10.1049/itr2.12176 10.1080/00423114.2020.1780455 10.1007/s12239-021-0129-9 10.1177/0954409716629705 10.5370/JEET.2016.11.4.1042 10.1504/IJMA.2022.120487 10.1109/87.930970 10.1080/15472450.2022.2046472 10.1109/TMECH.2022.3233705 10.1109/TVT.2022.3205452 10.5604/01.3001.0014.4234 10.1177/16878132221130574 10.1080/00423114.2018.1437273 10.1016/j.arcontrol.2004.02.004 10.1002/pamm.201710366 10.1049/iet-pel.2013.0882 10.1038/s41598-023-29526-8 10.3390/pr10122748 10.1109/TPEL.2020.2971637 10.1080/00423114.2014.881514 10.1109/TMAG.2018.2842433 10.1080/00423114.2002.11666243
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SubjectTerms	Active control Algorithms Control algorithms Control theory Controllers Deep learning Design Digital twins Dynamic models Efficiency H-infinity control Machine learning Neural networks Product enhancement Railway tracks Reinforcement Robotics Robust control Rotation Scale models Steering Trains Vehicles Wheels Wheelsets
Title	Active Steering Controller for Driven Independently Rotating Wheelset Vehicles Based on Deep Reinforcement Learning
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