Adaptive Fuzzy Control of Uncertain (P-R) Robot Manipulator using Lyapunov Method Compared to RLSE

This paper presents an adaptive fuzzy logic modeling and control (AFLC) of the Prismatic Revolute (P-R) manipulator associated with uncertainties. To estimate the uncertain parts of the process, fuzzy logic systems are used and to speed up the learning of fuzzy systems in order to extent the algorithm to online application and also reducing the error of the system against the unwanted changes and variation of the inputs parameters, recursive least square estimation (RLSE) method is introduced. Also to 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.