Decentralized tracking of a class of uncertain interconnected nonlinear systems using filter-driven approximators

Abstract

This paper investigates a decentralized tracker design problem using the filter-driven approximation technique in the presence of unknown interconnected nonlinearities. Compared with the existing recursive design methodologies using adaptive neural or fuzzy approximators for uncertain interconnected lower-triangular nonlinear systems, our main contribution is to develop the decentralized filter-driven approximation technique to compensate for unknown inherent and interconnected nonlinearities in the decentralized tracking scheme. The decentralized filter-driven approximators simply consist of a linear combination of first-order filtered signals of local state variables and a local control input, without nonlinear basis functions and weight adaptation laws used in the conventional adaptive function approximation technique. The stability analysis of the proposed decentralized tracking system is provided in the Lyapunov sense.