Optimization of Hydraulic Robot Arm Motion Control Algorithm Based on Neural Network Algorithm

The optimization of motion control algorithm can effectively improve the performance of robotic arms, which is of great significance to promote the application of hydraulic robotic arms in industrial automation. In this paper, an optimization method for the motion control algorithm of a hydraulic robotic arm based on the DDPG (Deep Deterministic Policy Gradient) algorithm is proposed combined with the dynamic characteristics of the hydraulic robotic arm, and a dynamic model of the hydraulic robotic arm is constructed. For the environmental model, deep neural networks are used to construct the actor network and commenter network of the DDPG algorithm to model and learn the actions and states of the hydraulic robotic arm. The problem of non-differentiability in the hybrid action of deterministic policy gradient methods is solved by adopting the chain rule of probability, and the motion control of the robotic arm is optimized through the self-learning ability by interaction with the environment. The test data shows that compared with the traditional control method, this method obtains favorable results in the optimization of motion control of hydraulic robotic arms by the DDPG algorithm, and effectively solves the problem of slow and unstable motion, low accuracy and poor controllability of the robotic arm.