This paper presents a novel decentralized tracking control system of electrically driven flexible-joint robots by adaptive type-2 fuzzy estimation and compensation of uncertainties. Owing to using voltage control strategy, the proposed control approach has important advantages over the torque control approaches in terms of being free from manipulator dynamics, computationally simple and decoupled. The design includes two interior loops: the inner loop controls the motor position while the outer loop controls the joint angle of the robot. An adaptive proportional–integral–derivative controller governs the outer loop, whereas a robust nonlinear controller supported by estimation of uncertainty is employed for the inner loop. More specifically, the main contribution of the paper arises from this fact that the proposed control method uses the interval Type-2 Fuzzy Logic systems for estimation of uncertainty. This is the main difference between this paper and those published in literature. One advantage of the proposed approach is that it uses available feedbacks as an important advantage from a practical point of view. The method is verified by stability analysis and its effectiveness is demonstrated by simulations. The direct method of Lyapunov is utilized for stability analysis of the proposed approach. The case of study is the tracking control of a three-joint articulated flexible-joint robot driven by permanent magnet DC motors. Simulation results show the superior robustness of the type-2 fuzzy system to Type-1 fuzzy system.
Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots
