Supervised control strategy in trajectory tracking for a wheeled mobile robot

Abstract

In this study, a single input interval type‐2 fuzzy logic (SI‐IT2‐FLC) supervised adaptive neural‐fuzzy interface system (ANFIS) controller is proposed for the velocity tracking task of a two‐wheeled mobile robot (WMR). The suggested technique can handle the inherent nonlinearities, uncertainties and external disturbances in the system model by a new supervised controller. The robot control design is accomplished by two separate phases including kinematic controller, which is characterized using the kinematic model of the robot, and dynamic controller, which is designed using the physical features of the robot dynamics. In particular, an SI‐IT2‐fuzzy PD (SI‐IT2‐FPD) controller is initially applied for the trajectory tracking problem in the two‐WMR. In this way, the impact of the footprint of uncertainty (FOU) on control surface (CS) generation is studied, i.e. several CSs were generated by changing a single coefficient which shapes the FOU. Then, a new SI‐IT2‐FPD supervised ANFIS controller is developed for the concerned robot system. To enhance the efficiency of the suggested controller, the baseline PD gains of the SI‐IT2‐FPD are adjusted in a heuristic manner. Finally, a prototype of the concerned robot is implemented to investigate the feasibility and applicability of the proposed framework in a real‐time platform.