Ayca Gokhan Ak

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Due to computational burden and dynamic uncertainty, the classical model-based control approaches are hard to be implemented in the multivariable robotic systems. In this paper, a model-free fuzzy sliding mode control based on neural network is proposed. In classical sliding mode controllers, system dynamics and system parameters are required to compute the(More)
The purpose of this paper is to propose adaptive fuzzy sliding mode control (SMC) based on radial basis function neural network (RBFNN) for trajectory tracking problem of three link robot manipulator. A RBFNN is used to compute the equivalent control of sliding mode control. A Lyapunov function is selected for the design of the SMC and an adaptive algorithm(More)
This paper presents an approach of cooperative control that is based on the concept of combining neural networks and the methodology of fuzzy sliding mode control (SMC). The aim of this study is to overcome some of the difficulties of conventional control methods such as controllers requires system dynamics in detailed. In the proposed control system, a(More)
The main problem of sliding mode controllers is that a whole knowledge system parameters is required to compute the equivalent control. Neural networks are used to compute the equivalent control. Standard two layer feedforward neural network training with the backprobagation algorithm and Radial Basis Function Neural Networks (RBFNN) are the most popular(More)
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