Underwater gliders linear trajectory tracking: The experience breeding actor-critic approach. (October 2022)
- Record Type:
- Journal Article
- Title:
- Underwater gliders linear trajectory tracking: The experience breeding actor-critic approach. (October 2022)
- Main Title:
- Underwater gliders linear trajectory tracking: The experience breeding actor-critic approach
- Authors:
- Zang, Wenchuan
Yao, Peng
Song, Dalei - Abstract:
- Abstract: This paper studies the underwater glider trajectory tracking in currents field. The objective is to ensure that trajectories fit to the straight target track. The underwater glider model is introduced to demonstrate the vehicle dynamic properties. Considering currents disturbance as well as the uncertain status of the glider controlled by complicated roll policies, the trajectory tracking task can be classified into the model-free optimization. Such problem is difficult to solve with mathematical analysis. This work transfers the underwater glider trajectory tracking into a Markov Decision Process by specifying the actions and observations as well as rewards. On this basis, a neural network controls framework called experience breeding actor-critic is proposed to handle the trajectory tracking. The EBAC enhances the explorations to the potentially high reward area. And it steers glider heading meticulously so as to counteract the currents influence. Through simulation results, the EBAC shows a desired performance in controlling the gliders to accurately fit the target track Highlights: The underwater glider simulator is established to develop algorithms and evaluate the effectiveness. The novel experience breeding actor-critic algorithm is applied for underwater gliders trajectory tracking. The optimizing framework of glider trajectory tracking from the Markov decision process perspective is proposed. The proposed method enhance the tracking accuracy compared withAbstract: This paper studies the underwater glider trajectory tracking in currents field. The objective is to ensure that trajectories fit to the straight target track. The underwater glider model is introduced to demonstrate the vehicle dynamic properties. Considering currents disturbance as well as the uncertain status of the glider controlled by complicated roll policies, the trajectory tracking task can be classified into the model-free optimization. Such problem is difficult to solve with mathematical analysis. This work transfers the underwater glider trajectory tracking into a Markov Decision Process by specifying the actions and observations as well as rewards. On this basis, a neural network controls framework called experience breeding actor-critic is proposed to handle the trajectory tracking. The EBAC enhances the explorations to the potentially high reward area. And it steers glider heading meticulously so as to counteract the currents influence. Through simulation results, the EBAC shows a desired performance in controlling the gliders to accurately fit the target track Highlights: The underwater glider simulator is established to develop algorithms and evaluate the effectiveness. The novel experience breeding actor-critic algorithm is applied for underwater gliders trajectory tracking. The optimizing framework of glider trajectory tracking from the Markov decision process perspective is proposed. The proposed method enhance the tracking accuracy compared with PID and SMC with the LOS guidance. … (more)
- Is Part Of:
- ISA transactions. Volume 129(2022)Part A
- Journal:
- ISA transactions
- Issue:
- Volume 129(2022)Part A
- Issue Display:
- Volume 129, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 129
- Issue:
- 2022
- Issue Sort Value:
- 2022-0129-2022-0000
- Page Start:
- 415
- Page End:
- 423
- Publication Date:
- 2022-10
- Subjects:
- Underwater gliders -- Linear trajectory tracking -- Oceanic currents interference -- Experience breeding actor-critic
Engineering instruments -- Periodicals
Engineering instruments
Periodicals
Electronic journals
629.805 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00190578 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.isatra.2021.12.029 ↗
- Languages:
- English
- ISSNs:
- 0019-0578
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4582.700000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24094.xml