A control method for intelligent loading and unloading machinery equipment based on improved deep deterministic strategy gradient algorithm

Due to the highly nonlinear, time-varying, and uncertain working environment of robotic arms, their dynamic behavior is complex. Traditional control methods often encounter problems such as large joint position angle errors and poor stability during the control process. Therefore, a control method based on the improved Deep Deterministic Policy Gradient (DDPG) is proposed. Firstly, establish a kinematic model of intelligent loading and unloading machinery equipment. Secondly, establish a loading and unloading action control model based on the DDPG algorithm. Thirdly, use the Actor network (strategy network) and Critic network (value network) to improve the loading and unloading action control process in DDPG. Finally, optimize the reward function, train the Actor network and Critic network to achieve loading and unloading machinery equipment control. The experimental results show that the actual motion trajectory of intelligent loading and unloading machinery equipment is very close to the expected trajectory, and the joint position error is always less than 5 cm, and the attitude angle error is always less than 1°. This can effectively control the joint position and attitude angle. The proposed improved DDPG algorithm has a good control effect.