Underwater gliders linear trajectory tracking: The experience breeding actor-critic approach. (October 2022) - Zang, Wenchuan; Yao, Peng; Song, Dalei
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 with PID and SMC with the LOS guidance.