Synthesis and Initialization of Adaptive Neuro-Fuzzy Control Based on the SDRE Technique for a Two-Link Manipulator

Abstract

One of the open problems in modern control theory is synthesis of adaptive control for nonlinear systems with parametric uncertainty and analysis of stability of the corresponding closed-loop system. Fuzzy logic is one of the approaches that can take into account nonlinearity and uncertainty of the plant. Affine systems constitute a class of nonlinear systems often used to solve various practical problems. For this class, there are a number of methods for synthesis of controllers, in particular, a method based on the matrix Riccati equation with state-dependent coefficients. In this paper, for a given class of nonlinear systems, an adaptation mechanism of a neuro-fuzzy controller that approximates the control synthesized by the SDRE method is used for the first time. The main contribution of this work is the architecture of the neuro-fuzzy network and methods for its initialization. The proposed approach is applied to the model of a two-link manipulator with uncertain coefficients. The conducted numerical experiments demonstrate the effectiveness of the synthesized control in terms of the selected quality criteria.