Resolving the Adaptation–Robustness Trade-Off: A Dual-Loop Optimal Feedback Control Architecture for BLDC Drives

Achieving a balance between rapid adaptation and robustness is a critical yet challenging objective in the design of industrial control systems. Model Reference Adaptive Control (MRAC) is a standard approach for managing system uncertainties; however, it suffers from a fundamental trade-off between adaptation speed and robustness. The high adaptation gains required for fast tracking often lead to parameter bursting or instability in the presence of noise. To resolve this issue, this paper proposes a new Dual-Loop Optimal Feedback Control (OFC) architecture applied to a Brushless DC (BLDC) motor drive. Unlike conventional methods that rely solely on tuning the adaptive mechanism, the proposed architecture introduces a parallel compensation loop designed to decouple disturbance rejection from reference tracking. This structure utilizes a Genetic Algorithm (GA) as an offline optimization engine to identify the Optimal Compensator gains that balance transient recovery with steady-state stability. Experimental validation demonstrates that the proposed Dual-Loop OFC architecture significantly outperforms traditional approaches. Specifically, it achieves an 88.99% reduction in overshoot and a 13.8% reduction in settling time compared to Conventional MRAC (CMRAC). Furthermore, it exhibits an 86.7% faster rise time compared to Self-Tuning Fuzzy PID (STFPID). These results confirm that the proposed Dual-Loop structure effectively mitigates the classic adaptability–robustness trade-off, offering a stable and high-performance solution for industrial actuators under varying operating conditions.