A novel reinforcement deep learning deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the quadrotor under study is derived, and the attitude control problem is described using formulas. Second, a sliding mode controller, including its sliding mode surface and reaching law, is chosen for the nonlinear dynamic system. The stability of the designed SMC system is validated through the Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adaptively adjust the switching control gain. During the training process, the input signals for the agent are the actual and desired attitude angles, while the output action is the time-varying control gain. Finally, the trained agent mentioned above is utilized in the SMC as a parameter regulator to facilitate the adaptive adjustment of the switching control gain associated with the reaching law. The simulation results validate the robustness and effectiveness of the proposed DDPG-SMC method.

To enhance the robustness of the nonlinear dynamic inverse (NDI) technique in the presence of model uncertainties, this study introduces a control scheme that integrates a reinforcement learning (RL) agent with the NDI approach. Initially, a fixed-wing unmanned aerial vehicle (UAV) is selected as the primary subject of investigation, and its attitude dynamics model is established. A control system employing a nonlinear disturbance observer within a dynamic inverse framework has been developed based on this model. Subsequently, the stability of the resulting control system is verified through Lyapunov analysis. Following this, a twin delayed deep deterministic policy gradient (TD3) agent is introduced, with the closed-loop system serving as the training environment. Through continuous interaction with its surroundings, the agent learns to dynamically adjust control parameters in response to control errors. Ultimately, the trained RL agent is utilized to optimize the control parameters for the dynamic system, and a flight simulation of the fixed-wing UAV’s attitude control is conducted. The simulation results demonstrate that the control parameters can be adaptively adjusted using the TD3-NDI method, which mitigates overshoot and suppresses oscillations during the control process. These findings confirm the effectiveness and robustness of the proposed control strategy.

A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

Capturing a noncooperative flying object is a challenging task that consists of two subtasks: precise tracking of the object and manipulation of the robotic grippers. High operation precision is required to avoid the object flying away from the gripper. In this article, we propose a bionic scheme to capture noncooperative flying objects imitating the predatory behavior of net-casting spiders. Based on this scheme, a robotic capturing system with a flexible net is developed for capturing noncooperative flying objects. The robotic capturing system consists of an unmanned aerial vehicle (UAV) platform, an optical tracking device, and a capture unit. These items imitate the body, median eye, and net of a net-casting spider. A scale adaptive tracking method is proposed to track noncooperative flying objects from video imagery collected by the optical tracking device used for guidance and control of the robotic capturing system. The capture unit deploys a net to entangle with the object. The proposed scheme is validated systematically to capture a noncooperative UAV and the experimental results demonstrated that the robotic capturing system has the advantages of robustness and adaptability.