FPGA design and implementation of fuzzy learning control: Application on DC motor position control

This paper investigates the implementation of a Fuzzy Model Reference Learning Control (FMRLC) on a Zedboard Zynq-7000 FPGA. The proposed adaptive controller dynamically adjusts its knowledge base and incorporates a memory-based control mechanism to retain and utilize past results in recurring situations. The design and deployment of the controller were carried out using the MATLAB/Simulink environment and applied to the angular position control of a DC motor. Initially, the controller was tested using the FPGA-In-the-Loop (FIL) approach to assess its robustness against disturbances in simulation. Subsequently, it was experimentally validated for real-time motor position control. The results obtained in FIL simulations and experimental tests demonstrate high tracking accuracy and strong disturbance rejection. These findings underscore both the superiority of the proposed controller over the conventional PID controller and the effectiveness of the adopted design methodology.