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<p>This study presents the design and simulation of a two-degree-of-freedom (2-DOF) PID controller aimed at precise speed regulation of DC motors. The controller is engineered to achieve two key objectives: accurate setpoint tracking and effective rejection of load disturbances. To optimize the controller parameters, a novel hybrid algorithm—Chaotic State of Matter Search integrated with Elite Opposition-Based Learning (CSMSEOBL)—is introduced. The resulting CSMSEOBL-optimized 2-DOF PID controller demonstrates enhanced performance, delivering fast disturbance mitigation and smooth tracking with minimal overshoot, outperforming conventional PID approaches in terms of robustness and precision. Optimization is guided by minimizing the Integral of Time-weighted Absolute Error (ITAE), serving as the fitness function. A comprehensive comparative analysis is conducted, evaluating the proposed method against a spectrum of control strategies, from classical PID designs to advanced intelligent algorithms. All modelling and simulation tasks are carried out using MATLAB/Simulink. Future research will focus on enhancing the algorithm’s adaptability to practical constraints, enabling dynamic parameter tuning, and embedding it within domain-specific heuristics to further improve optimization performance in engineering and automation applications.</p>

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Speed Control of a DC Motor Using Hybrid Metaheuristic-Tuned 2-DOF PID Controller

  • 期刊论文

  • 发表日期：

    2025年11月26日

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  • Neha Khanduja

摘要

This study presents the design and simulation of a two-degree-of-freedom (2-DOF) PID controller aimed at precise speed regulation of DC motors. The controller is engineered to achieve two key objectives: accurate setpoint tracking and effective rejection of load disturbances. To optimize the controller parameters, a novel hybrid algorithm—Chaotic State of Matter Search integrated with Elite Opposition-Based Learning (CSMSEOBL)—is introduced. The resulting CSMSEOBL-optimized 2-DOF PID controller demonstrates enhanced performance, delivering fast disturbance mitigation and smooth tracking with minimal overshoot, outperforming conventional PID approaches in terms of robustness and precision. Optimization is guided by minimizing the Integral of Time-weighted Absolute Error (ITAE), serving as the fitness function. A comprehensive comparative analysis is conducted, evaluating the proposed method against a spectrum of control strategies, from classical PID designs to advanced intelligent algorithms. All modelling and simulation tasks are carried out using MATLAB/Simulink. Future research will focus on enhancing the algorithm’s adaptability to practical constraints, enabling dynamic parameter tuning, and embedding it within domain-specific heuristics to further improve optimization performance in engineering and automation applications.