Energy-Efficient Nonlinear Controller Tuning for Mobile Robots via Metaheuristic Optimization

This study introduces a metaheuristic-driven optimization structure for the precise adjustment of nonlinear controllers in mobile robots. The main goal is to find controller gains that reduce both trajectory tracking error and energy usage at the same time. A metaheuristic optimization approach is utilized to address this multi-objective challenge, where the cost function integrates tracking precision, assessed by the Root Mean Square Error, and energy usage, determined as the integral of electrical power throughout the mission period, with a specific focus on minimizing energy in alignment with the conference theme. The suggested framework enhances the parameters of a nonlinear controller intended for tracking the trajectory of mobile robots. The metaheuristic algorithm repeatedly seeks optimal controller gains that provide the best balance between tracking effectiveness and energy efficiency, leading to a well-tuned controller. In contrast to traditional trial-and-error tuning techniques, this structured method guarantees functioning at an ideal equilibrium between accuracy and energy usage. Comprehensive simulations are performed across different trajectory scenarios to confirm the efficiency of the optimized controller. The resulting improvements are assessed using tracking precision and energy usage metrics, showing that the metaheuristic-driven tuning effectively meets the intended goals. The optimized controller achieves excellent tracking accuracy while greatly lowering energy usage in comparison to traditionally adjusted controllers. The results show that metaheuristic optimization offers a viable approach to achieving controller gains that reconcile the natural trade-off between energy efficiency and tracking performance. This research advances the area of energy-efficient autonomous systems by creating a structured tuning framework, with possible uses in battery-operated robotic platforms functioning in energy-limited settings.