Adaptive Fuzzy Control of 2DoF Uncertain Robot Manipulator using Lyapunov Method Compared to RLSE

This paper presents an adaptive fuzzy logic modeling and control (AFLC) of the nonlinear systems associated with uncertainties. Feedback Linearization method (FBL), used to linear the proposed dynamic. Also to estimate the uncertain parts of the process, fuzzy logic systems are used. To speed up the learning of fuzzy systems with in-line or off-line of proposed algorithm, and also for reducing the error of the system against the unwanted changes and variation of the inputs parameters, recursive least square estimation (RLSE) method is used as an innovation in this paper . For comparing the robustness of this method against uncertainties, the already implemented recursive least square learning method is compared with another adaptive controller based on Lyapunov algorithm (MIT-Rule). Finally the effectiveness of the proposed approach is verified in identification and control of the two degrees of freedom manipulator with uncertainties showing great performance on achieving the targets