Open Access

ARTICLE

Enhanced-WOA Optimized FOPID Controller for Energy-Efficient Path-Tracking Robot

Hooi Hung Tang, Te Meng Ting, Nur Syazreen Ahmad^*

School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia

* Corresponding Author: Nur Syazreen Ahmad. Email:

Computer Modeling in Engineering & Sciences 2026, 147(3), 1 https://doi.org/10.32604/cmes.2026.080428

Received 09 February 2026; Accepted 27 May 2026; Issue published 30 June 2026

Abstract

In industrial and service robotics, autonomous mobile robots must achieve accurate trajectory tracking while maintaining low energy consumption to avoid frequent recharging and performance degradation. Energy efficiency is particularly critical because locomotion accounts for 45%–65% of total power consumption, directly limiting operational range and autonomy. This paper proposes an energy-aware trajectory tracking framework that optimizes a fractional-order proportional-integral-derivative (FOPID) controller using an Enhanced Whale Optimization Algorithm (E-WOA). The key contributions are threefold: (1) the E-WOA hybridizes Differential Evolution (DE)’s global exploration with WOA’s local exploitation to overcome premature convergence in high-dimensional FOPID parameter spaces; (2) a composite fitness function jointly minimizes tracking error and energy consumption; and (3) controller parameters optimized on a single circular trajectory generalize effectively to complex paths without retuning. The proposed framework is evaluated on multiple trajectory configurations, including circular, eight-shaped, square, and rhombus paths, under stochastic environmental disturbances. Statistical analysis based on ten independent runs and validated using Analysis of Variance (ANOVA) was conducted against six classical and recent optimization methods: DE, WOA, Particle Swarm Optimization (PSO), Bat Algorithm (BA), Artificial Hummingbird Algorithm (AHA), and Rime Optimization Algorithm (RIME). The results show that E-WOA-FOPID achieves an average improvement of 15.14% in mean fitness and a 30.69% reduction in performance variability compared to the best-performing benchmark. These findings confirm its robustness as an energy-efficient solution for high-precision mobile robot trajectory tracking.

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