A modified Jaya-optimized decomposed fuzzy control approach for self-balancing two-wheel robots

This study develops a decomposed fuzzy controller for a self-balancing two-wheel robot, with the learning rates of the fuzzy modules optimized by a modified Jaya algorithm. The controller is structured into two interacting parts-one regulating the body tilt and the other compensating for wheel dynamics—to improve the handling of nonlinearities and reduce the computational load during real-time operation. The modified Jaya algorithm is incorporated to refine the adaptation process, enabling faster convergence and more reliable parameter updates under varying operating conditions. The proposed method is assessed through both simulation and hardware experiments. The experimental platform consists of an Arduino Mega ۲۵۶۰ board, an L۲۹۸N motor driver, and an MPU۶۰۵۰ sensor, which provides real-time attitude measurements for feedback control. Results show that the optimized fuzzy controller stabilizes the robot more quickly and maintains balance more effectively in the presence of disturbances compared with a conventional fuzzy design without optimization. These outcomes confirm the suitability of the modified Jaya–based optimization in enhancing the performance of decomposed fuzzy control for self-balancing robotic systems.

کلیدواژه ها:

Fuzzy controller ، decomposed fuzzy controller ، self-balancing two-wheel robot ، Adaptive control ، Jaya algorithm. rithm