Distributed Optimization for Continuous-Time Multi-Agent Systems with External Disturbance and Discrete-Time Communication

2015 ◽  
pp. 19-28 ◽  
Author(s):  
Zhenhua Deng ◽  
Yiguang Hong
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Distributed Optimization ◽  
External Disturbance ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

A modified distributed optimization method for both continuous-time and discrete-time multi-agent systems

2018 ◽  
Vol 275 ◽  
pp. 725-732 ◽  
Author(s):  
Dong Wang ◽  
Dong Wang ◽  
Wei Wang ◽  
Yurong Liu ◽  
Fuad E. Alsaadi
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Distributed Optimization ◽  
Optimization Method ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

scholarly journals A projection-based continuous-time algorithm for distributed optimization over multi-agent systems

2021 ◽  
Author(s):  
Xingnan Wen ◽  
Sitian Qin
Keyword(s):  
Continuous Time ◽  
Optimization Problem ◽  
Distributed Optimization ◽  
Optimal Solution ◽  
Fixed Time ◽  
Time Algorithm ◽  
State Variables ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

AbstractMulti-agent systems are widely studied due to its ability of solving complex tasks in many fields, especially in deep reinforcement learning. Recently, distributed optimization problem over multi-agent systems has drawn much attention because of its extensive applications. This paper presents a projection-based continuous-time algorithm for solving convex distributed optimization problem with equality and inequality constraints over multi-agent systems. The distinguishing feature of such problem lies in the fact that each agent with private local cost function and constraints can only communicate with its neighbors. All agents aim to cooperatively optimize a sum of local cost functions. By the aid of penalty method, the states of the proposed algorithm will enter equality constraint set in fixed time and ultimately converge to an optimal solution to the objective problem. In contrast to some existed approaches, the continuous-time algorithm has fewer state variables and the testification of the consensus is also involved in the proof of convergence. Ultimately, two simulations are given to show the viability of the algorithm.

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