基于强化学习补偿的地面无人战车行进间跟瞄自适应控制

Translated title of the contribution: Tracking and Aiming Adaptive Control for Unmanned Combat Ground Vehicle on the Move Based on Reinforcement Learning Compensation

Lianzhen Wei, Jianwei Gong*, Huiyan Chen, Zirui Li, Cheng Gong

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
386 Downloads (Pure)

Abstract

To deal with the nonlinear interference caused by chassis movement and road surface undulations with the tracking and aiming of unmanned combat ground vehicles, a tracking and aiming adaptive control method for unmanned combat ground vehicles on the move based on reinforcement learning compensation is proposed. This method consists of a main controller and a compensation controller. The main controller uses the PID control algorithm combined with the current tracking error to obtain the main control quantity, and the compensation controller uses the Dueling DQN reinforcement learning network to process the current state of the combat vehicle as well as the road surface undulation information near the local planning path to obtain the compensation control quantity. Firstly, the integrated kinematics model of the unmanned combat ground vehicle is established. Then, the compensation control algorithm based on reinforcement learning is described. Finally, simulation and verification are performed in three-dimensional scenes based on the V-REP dynamic software. The experimental results show that the tracking and aiming control method based on reinforcement learning compensation has good adaptive ability for chassis movement and road surface undulations, which effectively improves the tracking/aiming accuracy and stability of unmanned combat vehicles.

Translated title of the contributionTracking and Aiming Adaptive Control for Unmanned Combat Ground Vehicle on the Move Based on Reinforcement Learning Compensation
Original languageChinese
Pages (from-to)1947-1955
Number of pages9
JournalBinggong Xuebao/Acta Armamentarii
Volume43
Issue number8
DOIs
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • adaptive control
  • compensation control
  • reinforcement learning
  • tracking and aiming on the move
  • unmanned combat ground vehicle

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