Modeling, Estimation and Control of DC Motors: An Optimization Approach Using Hardware-In-The-Loop (HIL)

In industrial automation, achieving precise motor control is essential for ensuring accurate positioning and reliable operation. Traditional PID control methods often face limitations in handling nonlinearities and disturbances, creating a gap between simulation designs and real-world implementations. This study employs system identification techniques, including the NARX model in MATLAB, to model a 12 V DC motor controlled via an Arduino-based HIL setup. By leveraging the Spiral Dynamic Algorithm (SDA) for optimizing PID and 2DOFPID controllers, the research achieves improved performance metrics such as reduced steady-state error, faster settling times, and minimized overshoot. Performance evaluation demonstrates that the 2DOFPID controller significantly outperforms the conventional PID controller, particularly in terms of steady-state error on a step signal, showing a 20% improvement compared to the PID controller, along with reduced overshoot and improved settling time. Experimental validation further highlights the robustness of SDA-based controllers in adapting to dynamic industrial conditions. This research contributes to advancing control strategies tailored for DC motor positioning systems, with potential applications spanning peak and place robotics and industrial automation domains.