FPGA IP Core for DC Motor Control With Adaptive Neural Network PID Tuning, and High-Resolution Encoder Interface

Traditional proportional integral derivative (PID) falls short for precise control of DC motor speed under changing conditions. This paper presents a novel FPGA based IP (intellectual property) core for real-time PID parameter adjustment utilizing a multilayer neural network and the back propagation neural network algorithm. The design is implemented entirely in verilog HDL (hardware description language) using the Vivado 2023.1 tool in the Zynq 7z020 SoC (system on chip), with highly efficient resource utilization of 7.37% look-up table (LUT), 1.92% flip-flops, and 3.57% memory. The co-simulation results show a fast rise time of 0.00496s, a settling time of 0.0083s, a lower integral absolute error (IAE) of 0.045, and a minimum overshoot of 0.45%, validating the effectiveness of the proposed controller for comprehensive motion control systems. This dynamic approach outperforms traditional PID controllers by adapting to changing conditions, significantly improving rise time and settling time, which are critical factors in healthcare applications. Other key features of this work include precise duty cycle generation at a 10 kHz switching frequency for accurate motor speed control and an incremental encoder interface for high-resolution position feedback.