Title

Distributed Neural Observer-Based Formation Strategy of Non-Affine Nonlinear Multi-Agent Systems with Unknown Dynamics

Document Type

Article

Publication Date

1-1-2020

Publication Title

Journal of Circuits, Systems and Computers

DOI

10.1142/S0218126621300051

Keywords

Backpropagation learning algorithm, chaotic system, distributed neural observer, Multi-Agent System (MAS), nonaffine nonlinear systems

Abstract

© 2021 World Scientific Publishing Company. The state estimation in Multi-Agent Systems (MASs) is a challenging problem. This is due to the fact that (1) controlling nonaffine nonlinear MASs is a difficult task and also (2) the agents in MASs have direct impacts on each other. This paper presents a new distributed Neural Networks (NN) observer for the nonlinear dynamical model of MASs with nonaffine unknown dynamical agents. The proposed scheme uses the Backpropagation learning algorithm to estimate the unknown nonlinear functions of the agents. Compared with the previous studies, which primarily concentrated on the observer design for Multiple Input Multiple Output (MIMO) systems, the proposed method is applied to nonaffine nonlinear MASs. The advantages of this method are the overall stability, the fast convergence of the observer error to zero and the robustness against both uncertainties and disturbances. Nonlinear flexible-joint robots and nonlinear dynamic duffing chaotic systems are simulated to demonstrate the effectiveness and robustness of the proposed method. The proposed method is also compared with the Luenberger observer. The guaranteed stability, better performance in the presence of agents' uncertainties, robustness against disturbances are the main advantages of the proposed method compared with the traditional observer.

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