Deep Deterministic Policy Gradient (DDPG) Agent-Based Sliding Mode Control for Quadrotor Attitudes

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.