Online Adaptive Neural-PID Control for Performance Enhancement of DC Motor Speed Regulation

To achieve reliable speed control of DC motors, controllers should be capable of adapting in real time to nonlinearities, parameter variations, and sudden load changes. In this research study, an online Artificial Neural Network (ANN)-based PID controller is suggested to update its parameters during online operation continuously. As opposed to fixed controllers, the online ANN learns from the real-time system data and is therefore capable of tuning the PID gains in real time and maintaining high performance regardless of changing conditions. The control system is modeled and simulated in MATLAB/Simulink, and different scenarios are experimented with. The result demonstrates that the online ANN-PID controller delivers fast response, less overshoot, faster settling time, and insensitive tracking accuracy. Such adaptability guarantees online learning to be an efficient and dynamic solution to high-performance motor speed control in dynamic industrial operations.