Industrial robotic arms require a reliable ability to operate in the presence of unexpected perturbations. In this paper, control of a robotic arm is contemplated in the presence of parametric uncertainties which are coupled by significant external perturbations. The proposed method is based on tuning of the higher-order sliding mode controller parameters by the interval type 2 fuzzy logic. Initially, the common chattering effect of the classic sliding mode is eliminated by using higher-order SMC and saturation function, which makes it more robust than the classic algorithm. However, the high-order sliding mode still cannot deal with strong external perturbations properly. This issue is addressed by combining the algorithm with fuzzy type 2 membership functions which add the self-tuning feature to the controller. Simulations show the superiority of the proposed controller over the classic and the higher-order sliding mode controllers in dealing with various significant perturbations.
Industrial arms should be able to perform their duties in environments where unpredictable conditions and perturbations are present. In this paper, controlling a robotic manipulator is intended under significant external perturbations and parametric uncertainties. Type-2 fuzzy logic is an appropriate choice in the face of uncertain environments, for various reasons, including utilizing fuzzy membership functions. Also, using the neural network (NN) can increase robustness of the controller. Although neural network does not basically need to build its type-2 fuzzy rules, the initial rules based on sliding surface of higher order sliding mode controller (HOSMC) can improve the system's performance. In addition, self-regulation feature of the controller, which is based on the existence of the neural network in the central type-2 fuzzy controller block, increases the robustness of the method even more. Effective performance of the proposed controller (IT2FNN-HOSMC) is shown under various perturbations in numerical simulations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.