Design of PID Control Algorithm for Mechanical Arm Based on BP Neural Network Model

Abstract

The mechanical arm can directly replace the manual work to complete the repetitive mechanical production activities, and with the continuous improvement of the intelligent level, the requirements for the control accuracy of the mechanical arm are also improved. In order to achieve excellent control effect, PID must make the proportional, integral and differential control functions reach the best. Neural networks have the ability to express any nonlinearity and can achieve PID control with the best combination through systematic learning. Therefore, this article proposes a control method that combines BPNN (BP neural network) and PID control to control the robotic arm. By utilizing the characteristics of BPNN to adaptively modify the three parameters of the PID controller, the control effect of the PID controller can be improved. The simulation results show that the joint response does not oscillate after the optimization of BPNN, and all joints are stable around 1.7s and the overshoot is appropriate, which better achieves the control effect of the mechanical arm, indicating the effectiveness of BPNN in parameter optimization of the mechanical arm PID controller.