Application of Self-Tuning of PID Control Based on BP Neural Networks in the Mobile Robot Target Tracking

Artificial neural network (ANN), also known as parallel distributed processing model or connection mechanism model, is an information processing system or a computer system based on the structure and the ability to mimic the human brain [1]. BP neural network self-tuning PID controller combines BP neural network and the traditional PID control advantages which tuning PID three coefficients based on neural network in real time online learning [2]. This will give full play to their respective advantages, so as to broaden the applications of the PID control. Mobile robot as a controlled object modeling with BP neural network self-tuning PID control is conducted a simulation study of robot tracking moving objects. The simulation results show that: Tracking Performance of the BP neural network self-tuning PID controller is quite good. The experiments show that: this controller has better robustness and adaptability than traditional PID controller, which can meet the requirements of the mobile robot on the low-speed two-dimensional moving object tracking applications.