Distributed Feedback Optimization of Nonlinear Uncertain Systems Subject to Inequality Constraints
Zhengyan Qin, Tengfei Liu, Tao Liu, Zhong‐Ping Jiang, Tianyou Chai
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.