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Fully Distributed Sub-Optimal Coordination for Nonlinear Multi-Agent Systems
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DOI：10.1109/TASE.2026.3662783.png)
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Abstract
En 中文
This paper is concerned with the distributed coordination problem for the nonlinear multi-agent system (MAS) over a general digraph, where each agent is a multi-input multi-output system. The existing solutions are limited to the system without inputs coupling and with known, global Lipschitz, or linearly growing nonlinearities. To remove these requirements, we propose an integrated sub-optimal and control strategy for the more general nonlinear MAS. It consists of a fully distributed adaptive gradient optimization algorithm and a set of model-free prescribed performance controllers. Our approach ensures that the outputs of the MAS converge to the arbitrarily small neighborhoods of the optimal outputs; in particular, the reference-tracking performance is allowed to be freely predefined. Besides, the proposed control strategy is notably simple compared to the existing approaches, which typically employ function approximation, parameter identification, or derivative calculation. Finally, the simulation results illustrate the effectiveness and superiority of the proposed approach. Note to Practitioners—Many practical scenarios can be described as distributed optimal coordination problems for MASs, such as economic dispatch problem in power grid systems and optimal hose transportation problem using multiple quadrotors. Existing research has focused on linear MASs or nonlinear MASs with the known, globally Lipschitz or linearly growing system nonlinearities. However, numerous practical systems exhibit inherent nonlinearities, such as autonomous vehicles and robotic manipulators. Besides, the existing results are limited to single-input single-output (SISO) or multi-input multi-output (MIMO) systems without inputs coupling. Yet, coupled MIMO nonlinear MASs extensively exist in real-world applications. This provides significant motivation to investigate the distributed optimal coordination problem for the more general nonlinear MASs with input coupling and inherent nonlinearities. An integrated sub-optimal and control strategy is proposed, which consists of a fully distributed adaptive gradient optimization algorithm and a set of model-free prescribed performance controllers. This proposed strategy is applied to the source-seeking problem of a robotic network, illustrating its effectiveness and demonstrating its potential for practical engineering applications.
Keywords:
Distributed sub-optimal coordination
nonlinear multi-agent systems
unknown dynamics
inputs coupling
prescribed performance control
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