A Lyapunov stable type-2 fuzzy wavelet network controller design for a bilateral teleoperation system

Abstract

A bilateral teleoperation controller should make the system stable and achieve optimal performance in the presence of time delays, plant disturbances, measurement noise, modeling errors, and unknown environments. This paper proposes a new method of controller design based on a type-2 fuzzy wavelet neural network structure. A new algorithm based on gradient descent is used. The controller has the advantage of fuzzy controllers and wavelet neural network controllers to allow design of a bilateral teleoperation controller. The salient characteristics of the T2FWNN controller are that the system is constructed on the basis of type-2 membership functions to handle uncertainties associated with information and data in the knowledge base; it can handle unknown environmental interactions; it will remove most noise added to data transmitted through a communication channel. The algorithm of the proposed controller is Lyapunov stable and tracking error is negligible. The proposed controller is compared with a type-1 fuzzy wavelet neural network controller and a conventional fuzzy controller and simulation results show the efficacy of the proposed method.