A Fully Distributed Hybrid Control Framework For Non-Differentiable Multi-Agent Optimization  

作　　者：Xia Jiang Xianlin Zeng Jian Sun Jie Chen Yue Wei 

机构地区：[1]the Key Laboratory of Intelligent Control and Decision of Complex Systems,School of Automation,Beijing Institute of Technology,Beijing 100081 [2]the Beijing Institute of Technology Chongqing Innovation Center,Chongqing 401120,China [3]IEEE [4]the School of Electronic and Information Engineering,Tongji University,Shanghai 200082 [5]the Peng Cheng Laboratory,Shenzhen 518055,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第10期1792-1800,共9页自动化学报（英文版）

基　　金：supported in part by the NationalKey Research and Development Program of China(2021YFB1714800);the National Natural Science Foundation of China(61925303,62088101,62073035,62173034);the Natural Science Foundation of Chongqing(2021ZX4100027)。

摘　　要：This paper develops a fully distributed hybrid control framework for distributed constrained optimization problems.The individual cost functions are non-differentiable and convex.Based on hybrid dynamical systems,we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization methods.The proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump set.With the semistability theory of hybrid dynamical systems,the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different solutions.Numerical simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.

关 键 词：Distributed algorithm hybrid framework multiagent network non-differentiable optimization 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]