Data-driven Modeling and Control Strategy for DC Motor Performance Enhancement

This investigation introduces a novel approach for data-driven control and optimization of direct current (DC) motors. The strategy utilizes MATLAB/Simulink to simulate the behavior of a DC motor, allowing for precise estimation of its dynamics. The motor’s input voltage and resulting speed are crucial factors that are recorded and subsequently used for the system identification process. By utilizing the functionalities of the system identification toolkit, a systematic analysis of recorded data is performed, resulting in the transfer function for the motor. Utilizing a non-linear Autoregressive with Exogenous Inputs (NARX) network, trained simultaneously with the data, enhances the system’s ability to make accurate predictions. This approach offers a clear benefit for engineers and researchers in this field by equipping them with a mechanism for real-time DC-motor monitoring and performance forecasting. Besides, the proposed data-driven approach aids in regulating the dynamics of the motor mimicked by the transfer function of the motor, whereas the Proportional-Integral-Derivative (PID) controller is based on the core ideas of classical control theory. Considering the complexity of the motor and non-linearity, the dual technique has been utilized in this research. The Genetic Algorithm (GA) uses controller gains to maximize motor performance and acquire optimized results under various operating conditions. This all-encompassing strategy not only ensures excellent control, but also emphasizes the adaptability and freedom of the proposed methodology. The simulation results and practical relevance for large-scale application in DC motor systems show the efficiency and robustness of the proposed control model, which surpasses standard techniques and is adaptive to dynamic features. The proposed control model demonstrates significant improvements in system response with optimized control parameters yielding faster rise time, reduced settling times, and minimal overshoot, highlighting its robustness and adaptability for large-scale applications.