Therefore, it plays an increasingly important role in the field of practical intelligent control. Combining PID controller with neural network can meet the actual demand for response speed and stability in the control process. Neural network has the characteristics of self-learning, self-adaptive and good robustness, etc. The boom in artificial intelligence has led to an increasing focus on neural network control. Therefore, the limitations of conventional PID in engineering applications are becoming more and more obvious. The PID control algorithm is widely used in practical engineering, but when facing the nonlinear and time-varying characteristics of the controlled object, it has the problems of tedious parameter adjustment and poor nonlinear adaptability. Compared with MCU, the convergence speed is far more than three orders of magnitude, which proves its superiority. The results show that the proposed system can realize the self-tuning of PID control parameters, and also has the characteristics of reliable performance, high real-time performance, and strong anti-interference. A co-simulation of Modelsim and Simulink is used to simulate and verify the system, and a test analysis is also performed on the development platform. The pulse width modulation (PWM) signal generation module generates PWM waves with different duty cycles to control the rotation speed of the motor. The speed measurement module completes the acquisition of the output pulse signal of the encoder and the measurement of the motor speed. The peripheral modules of the control system are divided into two main parts.
The error backpropagation and weight update module completes the update of the weights of each layer of the network. The main state machine module generates enable signals that control the sequential execution of each sub-module. The PID module implements the mapping of PID arithmetic to register transfer level (RTL) and is responsible for completing the output of control amount. The forward propagation module is used to complete the forward propagation operation from the input layer to the output layer. In the design of the controller, it is divided into several sub-modules according to the modular design idea.
Therefore, in this paper, a closed-loop motion control system based on BP neural network (BPNN) PID controller by using a Xilinx field programmable gate array (FPGA) solution is proposed. However, the commonly used microcontroller unit (MCU) cannot meet the application scenarios of real time and high reliability. In the actual industrial production process, the method of adaptively tuning proportional–integral–derivative (PID) parameters online by neural network can adapt to different characteristics of different controlled objects better than the controller with PID.
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