Optimization of saturated super-twisting sliding mode guidance law using reinforcement learning algorithm

This paper proposes a novel missile guidance law optimization method based on deep reinforcement learning, specifically targeting terminal guidance for missiles engaging highly maneuverable targets in near-space environments. In scenarios where both the missile and target have comparable overload capabilities, effective interception becomes a significant challenge. Existing methods, such as the Saturated Super-Twisting Algorithms, demonstrate strong performance in maneuvering target interception but face difficulties in parameter tuning and control input saturation. To overcome these limitations, this study introduces the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to optimize the parameters of missile guidance laws, offering an innovative solution to these complex challenges. The TD3 algorithm, known for its ability to handle noisy environments and mitigate Q-value overestimation, enhances the guidance system's capability to intercept highly maneuverable targets with greater precision. Simulation results validate the proposed approach, demonstrating a substantial performance improvement over traditional methods, thus providing both theoretical and practical contributions to missile guidance system optimization for next-generation missile defense applications.