ANFIS Control of DC Motor Implementation

This paper discusses the implementation of ANFIS (Adaptive Neuro-Fuzzy Inference System) control for DC motors. ANFIS is a powerful soft computing tool that combines neural network learning capabilities with fuzzy logic reasoning, making it particularly suitable for advanced control systems. DC motors are widely used across various industrial applications where precise control of speed and torque is critical. The ANFIS controller achieves these objectives through its hybrid learning algorithm that uses backpropagation for parameter optimization and least squares estimation for rule consequent tuning. The implementation typically involves creating a fuzzy inference system with Gaussian membership functions and training it using input-output data pairs. Key MATLAB functions used include anfis() for system training, evalfis() for inference operations, and gensurf() for surface visualization. The training process optimizes both premise parameters (membership functions) and consequent parameters (output linear functions) to minimize the error between desired and actual motor responses. Our research investigates DC motor control performance using ANFIS methodology and evaluates its effectiveness through simulation studies. The results demonstrate that ANFIS control significantly improves DC motor performance in terms of response time, overshoot reduction, and disturbance rejection compared to conventional PID controllers. The system shows particular effectiveness in handling non-linear motor characteristics and parameter variations.