Controller Design of Tracking WMR System Based on Deep Reinforcement Learning

Traditional PID controllers are widely used in industrial applications due to their simple computational architecture. However, the gain parameters of this simple computing architecture are fixed, and in response to environmental changes, the PID parameters must be continuously adjusted until the system is optimized. This research proposes to use the most important deep reinforcement learning (DRL) algorithm in deep learning as the basis and to modulate the gain parameters of the PID controller with fuzzy control. The research has the ability and advantages of reinforcement learning and fuzzy control and constructs a tracking unmanned wheel system. The mobile robotic platform uses a normalization system during computation to reduce the effects of reading errors caused by the wheeled mobile robot (WMR) of environment and sensor processes. The DRL-Fuzzy-PID controller architecture proposed in this paper utilizes degree operation to avoid the data error of negative input in the absolute value judgment, thereby reducing the amount of calculation. In addition to improving the accuracy of fuzzy control, it also uses reinforcement learning to quickly respond and minimize steady-state error to achieve accurate calculation performance. The experimental results of this study show that in complex trajectory sites, the tracking stability of the system using DRL-fuzzy PID is improved by 15.2% compared with conventional PID control, the maximum overshoot is reduced by 35.6%, and the tracking time ratio is shortened by 6.78%. If reinforcement learning is added, the convergence time of the WMR system will be about 0.5 s, and the accuracy rate will reach 95%. This study combines the computation of deep reinforcement learning to enhance the experimentally superior performance of the WMR system. In the future, intelligent unmanned vehicles with automatic tracking functions can be developed, and the combination of IoT and cloud computing can enhance the innovation of this research.