Trajectory Tracking Control Based on Deep Reinforcement Learning for a Robotic Manipulator with an Input Deadzone

Abstract

This paper proposes a deep reinforcement learning (DRL) method that combines random network distillation (RND) and long short-term memory (LSTM) to address the tracking control problem, while leveraging the inherent symmetry in robotic arm movements to eliminate the need for learning or knowing the system’s dynamic model. In general, the complexity and strong coupling of robotic manipulators make trajectory tracking extremely challenging. Firstly, the prediction network and fixed network are jointly trained using the RND method. The difference in output values between the two networks acts as an internal reward for the robotic manipulator environment. This internal reward mechanism encourages the robotic arm agent to actively explore unpredictable and unknown environmental states, thereby consequently boosting the performance and efficiency of the tracking control for the robotic manipulator. Then, the Soft Actor-Critic (SAC) algorithm, the LSTM network, and the attention mechanism are integrated to resolve the instability problem during training and acquire a stable policy. The LSTM model effectively captures the symmetry and temporal changes in joint angles, while the attention mechanism dynamically prioritizes important features, thereby reducing the instability of the robotic manipulator during tracking tasks and enhancing feature extraction efficiency. The simulation outcomes demonstrate that the proposed method effectively performs the robot tracking task, confirming the efficacy and efficiency of the DRL algorithm.