Decentralized Observer Design for Heterogeneous Multi-agent Systems with Fault Bound Estimation

2019 ◽  
Author(s):  
Chun Liu ◽  
Bin Jiang ◽  
Ke Zhang
Keyword(s):  
Observer Design ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Bound Estimation ◽  
Decentralized Observer

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

2020 ◽  
pp. 2130005 ◽  
Author(s):  
Pejman Manouchehri ◽  
Reza Ghasemi ◽  
Alireza Toloei ◽  
Fazel Mohammadi
Keyword(s):  
Mimo Systems ◽  
Learning Algorithm ◽  
Multiple Input Multiple Output ◽  
Observer Design ◽  
Multi Agent Systems ◽  
Observer Error ◽  
Agent Systems ◽  
Luenberger Observer ◽  
Flexible Joint Robots ◽  
Multi Agent

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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