Genetic Algorithm-Optimized Fractional-Order SMC for Wheeled Robots
Résumé
This paper presents a Genetic Algorithm-Optimized Fractional-Order Sliding Mode Control (GA-FOSMC) approach for trajectory tracking of wheeled robots, focusing on disturbance compensation. The proposed method aims to enhance the tracking accuracy of wheeled robots while mitigating the effects of external disturbances such as external forces, sensor noise, and model inaccuracies. A fractional-order sliding mode control (FOSMC) framework is designed, incorporating a disturbance vector to adjust the control inputs for robust performance. The controller parameters are optimized using a Genetic Algorithm (GA) to minimize the tracking error. The simulation results demonstrate that the proposed GA-FOSMC controller effectively compensates for disturbances, ensuring precise trajectory tracking while maintaining stability. The approach is validated through simulation, showing its potential for real-world robotic applications where disturbance rejection and optimal performance are essential.
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