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Volume 9 Issue 7
Jul.  2022

IEEE/CAA Journal of Automatica Sinica

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F. Tatari, H. Modares, C. Panayiotou, and M. Polycarpou, “Finite-time distributed identification for nonlinear interconnected systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1188–1199, Jul. 2022. doi: 10.1109/JAS.2022.105683
Citation: F. Tatari, H. Modares, C. Panayiotou, and M. Polycarpou, “Finite-time distributed identification for nonlinear interconnected systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1188–1199, Jul. 2022. doi: 10.1109/JAS.2022.105683

Finite-Time Distributed Identification for Nonlinear Interconnected Systems

doi: 10.1109/JAS.2022.105683
Funds:  This work was partially supported by the European Union’s Horizon 2020 research and innovation programme (739551) (KIOS CoE), and from the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development
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  • In this paper, a novel finite-time distributed identification method is introduced for nonlinear interconnected systems. A distributed concurrent learning-based discontinuous gradient descent update law is presented to learn uncertain interconnected subsystems’ dynamics. The concurrent learning approach continually minimizes the identification error for a batch of previously recorded data collected from each subsystem as well as its neighboring subsystems. The state information of neighboring interconnected subsystems is acquired through direct communication. The overall update laws for all subsystems form coupled continuous-time gradient flow dynamics for which finite-time Lyapunov stability analysis is performed. As a byproduct of this Lyapunov analysis, easy-to-check rank conditions on data stored in the distributed memories of subsystems are obtained, under which finite-time stability of the distributed identifier is guaranteed. These rank conditions replace the restrictive persistence of excitation (PE) conditions which are hard and even impossible to achieve and verify for interconnected subsystems. Finally, simulation results verify the effectiveness of the presented distributed method in comparison with the other methods.

     

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    Highlights

    • A novel finite-time distributed identification method is introduced for nonlinear interconnected systems using concurrent learning
    • The concurrent learning approach continually minimizes the identification error for a batch of previously recorded data collected from each subsystem as well as its neighboring subsystems
    • The state information of neighboring interconnected subsystems is acquired through direct communication
    • The overall update laws for all subsystems form coupled continuous-time gradient flow dynamics for which finite-time Lyapunov stability analysis is performed
    • The restrictive persistence of excitation (PE) conditions are replaced with easy-to-check rank conditions on data stored in the distributed memories of subsystems, under which finite-time stability of the distributed identifier is guaranteed

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