Soft Actor-Critic Based Deep Reinforcement Learning Strategy for Tracking Control of Unmanned Aerial Vehicles.
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| Title: | Soft Actor-Critic Based Deep Reinforcement Learning Strategy for Tracking Control of Unmanned Aerial Vehicles. |
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| Authors: | Chen, Bo1 njit_airobot@126.com, Song, Chengcheng2 schengcheng@126.com, Zhou, Baoyu1 y00452505136@njit.edu.cn, Wu, Hao1 y00452505127@njit.edu.cn, Shang, Yanling3 hnnhsyl@126.com, Li, Guipu4 ligp@njit.edu.cn, Gao, Fangzheng5 gaofz@126.com |
| Source: | Engineering Letters. Sep2026, Vol. 34 Issue 9, p3917-3925. 9p. |
| Subjects: | Reinforcement learning, Tracking control systems, Model validation, Markov processes, Maximum entropy method, Drone aircraft, Robust control |
| Abstract: | To enhance the autonomous target tracking and control performance of unmanned aerial vehicles (UAVs) in complex and dynamic environments, this paper proposes a deep reinforcement learning-based UAV tracking control method. First, the UAV target tracking problem is formulated as a Markov Decision Process (MDP), with carefully designed state space, action space, and reward function. On this basis, the Soft Actor-Critic (SAC) algorithm is introduced to construct the UAV control policy. By leveraging the maximum entropy reinforcement learning framework, the proposed approach improves exploration capability and training stability. Finally, the effectiveness of the method is validated in a simulation environment. Experimental results demonstrate that the proposed method can achieve stable target tracking under various initial conditions and target motion patterns, while exhibiting strong robustness and generalization performance. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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