Design and Analysis of Adaptive Fuzzy Super-Twisting Sliding Mode Controller for Uncertain 2-DOF Robotic Manipulator

This paper proposes a robust adaptive fuzzy supertwisting sliding mode controller (AFST-SMC) to enhance the trajectory tracking capability of a 2-DOF robotic manipulator. The novel controller compensates for system uncertainties by adaptively adjusting control parameters based on the angular position of the target joints. To evaluate the controller’s performance, the dynamic model of a two-link manipulator is derived using the Lagrangian method, and stability analysis of the system is considered according to the Lyapunov Direct Method. Modeling and simulation are performed in Matlab®/Simulink®. Performance is assessed by comparing the proposed AFST-SMC with conventional sliding mode controller (SMC), super-twisting sliding mode controller (ST-SMC), and adaptive fuzzy sliding mode controller (AF-SMC), using integral absolute error (IAE) as a performance criterion. The simulation results indicate that the AF-STSMC successfully reduces the tracking error. For link 1, the IAE is reduced to 0.01465 rad, representing a 99.9% reduction compared to SMC (14.24 rad), an 85% reduction compared to ST-SMC (0.0977 rad), and a 78.5% reduction compared to AF-SMC (0.06819 rad). Similarly, in link 2, the AF-STSMC gives an IAE of 0.003862 rad that is equivalent to a 99.93% decrease with respect to SMC (5.723 rad), a 98.7% increase over ST-SMC (0.306 rad), and a 91.2% decrease compared to AF-SMC (0.04391 rad). It is therefore clear that the designed AFST-SMC performs better than the controllers in terms of accuracy and robustness to achieve the required performance specifications of accurate trajectory tracking by robotic manipulators.