Reinforcement Learning based position control of shell-fetching manipulator with extreme random trees

Abstract

The automatic loading system of artillery includes the gun-fetching manipulator, which is crucial. The stability and control accuracy of the manipulator, however, are comparatively subpar when following the position trajectory as a result of the changes in the settings of the permanent magnet synchronous motor. This research suggests a reinforcement learning-based strategy for controlling a gun manipulator’s motor behavior in light of the current scenario. In this algorithm, the state variable is the feedback output of the control variable, and the reward function is utilized to calculate the associated reward value. The reinforcement learning agent then makes decisions based on the environment’s state and reward value, adjusting the manipulator’s trajectory in real-time. By comparing different simulation results, the gun-taking manipulator can achieve more accurate position trajectory control based on reinforcement learning.