How Can a Fuzzy Controller Save Energy in Motors?

Direct current motors power robots, 3D printers and drones, but they often waste energy when moving between points. Researchers tackled this by using a fuzzy controller based on the Tsukamoto inference method. Unlike the classic PID, the fuzzy controller selects actions through linguistic rules (e.g., “if speed is high and acceleration low, then reduce voltage”). They also paired it with smoother motion profiles: parabolic, trapezoidal and S‑curves.

Tests showed that, with no load, average energy consumption dropped by 11.8% for the parabolic profile, 9.3% for the trapezoidal one and 3.5% for the S‑curve. Adding a load kept the savings similar. Statistical analysis (Welch’s t‑test, p < 0.05) and lower data dispersion confirmed the results compared to PID.

In short, a fuzzy controller can make motors “gentler” on energy without losing accuracy, a promising tip for educational robotics, lightweight electric vehicles or any device needing fast yet efficient movements.

The Tsukamoto fuzzy controller employs monotonic membership functions and a defuzzification process based on the weighted average of conclusions. This yields a deterministic output, unlike Mamdani systems that need a centroid step. In motion control, S‑curve trajectories lower acceleration derivatives (jerk), reducing Joule heating losses in conductors. Energy consumption was measured by integrating voltage times current over each motion cycle. Consistency was assessed via the coefficient of variation (CV = σ/μ), which was lower for the fuzzy controller, indicating higher stability. Welch’s t‑tests, chosen for robustness to unequal variances, confirmed statistical significance (p < 0.05). The hardware implementation used a 32‑bit microcontroller with floating‑point capability, allowing real‑time fuzzy rule execution (~150 µs per cycle) without compromising system dynamics.