Achieving accurate trajectory tracking in Active Denial System (ADS) remains a complex and pressing task, particularly for real-time tracking-and-pointing operations. In this context, we begin by examining the stringent accuracy demands of such systems and put forward an adaptive control strategy that integrates Integral Sliding Mode Control (ISMC) with an improved Double Deep Q-Network (AdaDDQN). The proposed method employs a high-dimensional state-space framework coupled with a context-aware reward mechanism, enabling the controller to adaptively fine-tune its parameters on the fly. This hybrid approach harnesses the learning flexibility of Reinforcement Learning (RL) while preserving the inherent stability of SMC. Experimental evaluations reveal that this method achieves a significant improvement—over 80% gain in tracking precision compared to traditional sliding mode schemes—maintaining the tracking error consistently within 8 mrad. Moreover, it demonstrates strong resilience against mechanical friction, external interference, and model uncertainties, ensuring both accuracy and robustness under real-world operational constraints.
Sun et al. (Sat,) studied this question.