Master–Slave Architecture Enhanced and Improved <scp>GBO</scp> Tuned Cascaded <scp>PI</scp>‐<scp>PDN</scp> Controller for Speed Regulation of <scp>DC</scp> Motors

ABSTRACT This study introduces a novel master–slave architecture featuring an improved gradient‐based optimizer (ImGBO) to effectively tune a cascaded proportional‐integral (PI) and proportional‐derivative with filter (PDN) controller specifically for DC motor speed regulation. The core novelty of this work lies in enhancing the traditional GBO algorithm by integrating an experience‐based perturbed learning mechanism and an adaptive local search strategy, significantly enhancing its ability to balance exploration and exploitation during optimization. The proposed ImGBO‐based cascaded PI‐PDN controller is comprehensively evaluated against traditional GBO, recent metaheuristics and advanced proportional‐integral‐derivative (PID) and fractional‐order PID (FOPID) controllers. Significant improvements were observed, with the proposed method demonstrating exceptionally short rise (0.0089 s) and settling times (0.0140 s), no overshoot, and minimal steady‐state error (0.0017%). Stability analysis via pole placement and Bode plots affirmed the robust and stable operation of the controller, exhibiting a phase margin of 71.6640° and infinite gain margin. These results strongly support the suitability and effectiveness of the ImGBO‐based approach for precision‐critical DC motor control applications.