Abstract
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.
Highlights
Los brazos industriale deben poder realizar sus tareas en entornos donde existen condiciones y perturbaciones impredecibles
Delay which is a typical problem for conventional Sliding mode control (SMC) has been addressed by implementing higher order sliding mode controller (HOSMC) as input block for the main controller
It should be noted that the reason of unsuitable performance of the SMC is that the frequency of the perturbations is deliberately selected as small as it could cause problem for this method
Summary
Los brazos industriale deben poder realizar sus tareas en entornos donde existen condiciones y perturbaciones impredecibles. The employed switch function might be classic sliding mode and the type-II fuzzy logic connects various control algorithms as a unique system. (Nekoukar & Erfanian, 2011) have proposed an adaptive learning algorithm and a fuzzy logic system to estimate dynamic system for controlling a robotic operator using sliding mode method. Niknam et al (Niknam, Khooban, Kavousifard, & Soltanpour, 2014) have proposed an optimal controller by combining type-II fuzzy logic and sliding mode for controlling a specific class of nonlinear systems To this end, particle swarm optimization algorithm has been employed for adjusting parameters of input and output membership functions. IT2NN (neural network based on type-2 fuzzy logic) is capable to handle control task in presence of uncertainty, it is not able to cover external perturbations with any domain.
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