Abstract

Brushless DC motors have a very wide range of applications in industrial automation, however traditional PID controllers cannot provide stability with nonlinear and noisy operating conditions. To overcome this, a novel optimized Nelder–Mead algorithm is employed for an intelligent control structure using a Fractional Order PID controller, targeting the minimization of the Integral Time-weighted Absolute Error (ITAE). The proposed NM-FOPID framework is benchmarked against industrial standards, including Ziegler–Nichols (ZN) and Cohen–Coon (CC) methods, as well as metaheuristic benchmarks like PSO and GA. Experimental validation under different loading conditions (0.5–2.0 kg·m²) showed substantial performance improvement over traditional PID control. The FOPID controller reduced overshoot from 48.3% (traditional PID) to 8.24%, reduced settling time from 31.8 s to 5.9 s (≈ 81% improvement), and raised damping ratio to 1.73, leading to more robustness against disturbances. A White-noise test and frequency-domain analysis also verified high gain stability with a 25° phase margin. The proposed FOPID-based control realizes 70–80% improvement in transient performance and noise robustness, providing an optimal, Industry 4.0-compatible solution to smart BLDC motor control. Finally, the framework is implemented on a Raspberry Pi platform with Firebase integration, providing a scalable Industry 4.0 solution. The IoT layer achieves a measured jitter of ±1.2 ms and an 85 ms cloud latency, successfully decoupling high-speed local regulation from remote monitoring. These findings confirm that the NM-optimized FOPID provides a resilient, energy-efficient, and practical alternative for high-performance electric drive systems.

Keywords

Brushless DC Motor, Pulse Width Modulation, Fractional Order PID Controller, Nelder–Mead (NM) Simplex Optimization Algorithm,

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References

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