Distributed Resource Allocation for Nonlinear Multi-Agent Systems via Feedback Optimization

Zhengyan Qin

doi:10.1109/tac.2026.3724467

Distributed Resource Allocation for Nonlinear Multi-Agent Systems via Feedback Optimization

2026-08-17

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FENG, Gang (Gang Feng)

DOI：10.1109/tac.2026.3724467

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This paper addresses the nonconvex distributed resource allocation problem for uncertain nonlinear multi-agent systems. The objective is to design distributed real-time gradient feedback protocols that steer the agents' outputs to the set of Karush-Kuhn-Tucker (KKT) points. Unlike conventional offline algorithms that require explicit analytical expressions of the gradients of local objective functions, the proposed protocols use only gradient values along agent trajectories. With the exchange of this local gradient information among neighbors, two classes of distributed real-time gradient feedback protocols are proposed. The first employs a constant feedback gain and guarantees convergence provided that the feedback gain is chosen below an explicit bound. The second adopts a vanishing time-varying feedback gain, requiring no prior knowledge of parameters associated with the agents, local objective functions, or communication graph, and enabling its fully distributed implementation. The results are further extended to jointly optimizing the agents' inputs and outputs when each open-loop system admits a desired input–output property. The effectiveness and advantages of the protocols are demonstrated through a numerical example on economic optimization of interconnected chemical reactors.

Keywords:

Distributed resource allocation

distributed feedback optimization

steady-state control

real-time gradient

time-varying protocols

nonconvex optimization

Distributed Resource Allocation for Nonlinear Multi-Agent Systems via Feedback Optimization

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