Distributed optimization of general linear multi-agent systems with external disturbance

Li, Shiling; Nian, Xiaohong; Deng, Zhenhua

doi:10.1016/j.jfranklin.2021.05.024

Cited by 16 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Today, distributed algorithms are designed for general linear multi-agents systems. However, the existing algorithms in [52]- [54] can only solve the resource allocation problem with a global equality constraint and cannot directly be extended to local inequality constraints with the projection operator. Moreover, Assumption 1 is weaker than the assumption in [50], [52]- [54], that is, rank…”

Section: A State-based Algorithmmentioning

confidence: 99%

“…Example 1. A network with 6 agents over a ring communication network is considered [22], [52]. The matrix parameters are set as…”

Section: Example Analysismentioning

confidence: 99%

“…In contrast to [47], a penalty-function-based optimal resource allocation algorithm was proposed in [49]. Based on a stronger rank condition in [50], [51], the distributed resource allocation problem with global linear coupling constraint over the general linear multi-agent systems was solved in [52] for the case that dynamics of agents are subject to the unknown external disturbances. A predefined-time convergent distributed optimization algorithm was designed using a time-based generator to accelerate the convergence speed [53].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the time-varying adaptive weights are taken into consideration in this paper for removing the knowledge of the Laplacian matrix. Although the distributed resource allocation problem is also studied in [52]- [54], they do not take into account the local constraint. the existing projection-based algorithms and distance penalty function method, our proposed algorithm is more efficient for the complex local constraints.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed Resource Allocation Algorithm for General Linear Multiagent Systems

Shao

Shi

2022

IEEE Access

View full text Add to dashboard Cite

We focus on the optimal resource allocation problems with global equality constraints and convex function inequality constraints over heterogeneous linear multi-agent systems. The resource allocation problem aims to minimize the total objective function through neighboring information exchange. First, based on the state variable information, Karush-Kuhn-Tucker(KKT) conditions, and proportionalintegral control concept, we propose an initialization-free distributed optimization algorithm, where each agent is driven by the gradient(subgradient) of its local objective function and local constraint convex function. In addition, the penalty factor control parameter is changed adaptively. Next, we propose an output-based distributed optimization algorithm that uses a Luenberger observer when the state variable is not accessible. Based on Lyapunov stability, it is proved that the proposed algorithms converge to the optimal solution of the resource allocation problem. Finally, simulation examples are used to demonstrate the effectiveness of the proposed algorithms.

show abstract

Section: A State-based Algorithmmentioning

confidence: 99%

“…Example 1. A network with 6 agents over a ring communication network is considered [22], [52]. The matrix parameters are set as…”

Section: Example Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Resource Allocation Algorithm for General Linear Multiagent Systems

Shao

Shi

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…erefore, several studies try to design distributed algorithms for the linear and nonlinear second-order multiagent systems [19][20][21][22][23]. Based on the tracking control idea, the resource allocation with equality constraint is researched in [24] for the general linear heterogeneous multiagent systems. For the same problem, a predefined time-convergent distributed optimization algorithm is designed in [25], in which a time-varying control parameter is utilized for obtaining higher feedback gain.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Resource Allocation Algorithm for the General Linear Multiagent Systems

Shao

Shi

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

We study the distributed resource allocation problem for heterogeneous multiagent systems over an undirected graph, an essential issue in multiagent system coordinated control and complex network system control. The decision variable is subject to global equality and local convex set constraints, and the objective is smooth and convex. It aims to minimize the global objective function by exchanging neighboring information between agents. An adaptive distributed algorithm is designed using the distance function-based exact penalty function method. A state- and time-based triggering condition is designed to avoid continuous communication and reduce the communication burden. From a random initial state, the proposed algorithm asymptotically converges to the optimal value by means of LaSalle’s invariance principle. Finally, examples are provided to demonstrate the effectiveness of our algorithm.

show abstract

Event-Triggered Consensus Tracking for Time-Delay Multi-Agent Systems with Input Saturation Via Dynamic Output Feedback

Li,

2023

J Intell Robot Syst

View full text Add to dashboard Cite

Distributed optimization of general linear multi-agent systems with external disturbance

Cited by 16 publications

References 32 publications

Distributed Resource Allocation Algorithm for General Linear Multiagent Systems

Distributed Resource Allocation Algorithm for General Linear Multiagent Systems

A Distributed Resource Allocation Algorithm for the General Linear Multiagent Systems

Event-Triggered Consensus Tracking for Time-Delay Multi-Agent Systems with Input Saturation Via Dynamic Output Feedback

Contact Info

Product

Resources

About