Distributed Feedback Optimization of Nonlinear Uncertain Systems Subject to Inequality Constraints

Zhengyan Qin, Tengfei Liu, Tao Liu, Zhong Ping Jiang, Tianyou Chai

Research output: Contribution to journalArticlepeer-review

Abstract

This paper studies the distributed feedback optimization problem for nonlinear uncertain multi-agent systems subject to inequality constraints. A new class of distributed optimization algorithms is proposed by extending the standard primal-dual dynamics and introducing two new inputs to deal with the couplings arising from feedback optimization. With each controlled agent satisfying a mild dissipation assumption, the proposed distributed feedback optimization algorithms, using only the output-dependent gradient value of each agent's corresponding local objective function and the information from its neighboring agents, can steer the outputs of the agents to a common set-point which minimizes the total objective function while satisfying the inequality constraints. A composite Lyapunov function is constructed to prove global asymptotic stability of the closed-loop system at the equilibrium corresponding to the optimal point.

Original languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalIEEE Transactions on Automatic Control
DOIs
StateAccepted/In press - 2023

Keywords

  • Distributed feedback devices
  • Distributed feedback optimization
  • Heuristic algorithms
  • Linear programming
  • Multi-agent systems
  • Optimization
  • Power system dynamics
  • Uncertain systems
  • inequality constraints
  • nonlinear systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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