Master-slave architecture enhanced and improved gbo tuned cascaded pi-pdn controller for speed regulation of dc 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 degrees and infinite gain margin. These results strongly support the suitability and effectiveness of the ImGBO-based approach for precision-critical DC motor control applications.