Predefined-time distributed optimization of general linear multi-agent systems

Consensus of a novel heuristic nonlinear multi-agent system in DOS attack network environment via saturation impulse control mechanism

This paper mainly studies the consensus control strategy for a novel heuristic nonlinear multi-agent system. Compared with most existing related researches, firstly, the novel heuristic nonlinear multi-agent system has the ability to construct its communication network topology heuristically, and can withstand long-term DOS(Denial of Service) attacks, with the advantages of high practicality and security. Secondly, in order to control the multi-agent system, a control protocol based on both saturation effect and impulse control mechanism is studied, which has the advantages of high efficiency, low cost and wide applicability. Thirdly, for the multi-agent system, its dynamic model is constructed and analyzed by Lyapunov stability theory and matrix measure theory, and some sufficient conditions for achieving consensus are obtained. Finally, through two simulation experiments and some corresponding comparative analysis, the correctness, efficiency, and superiority of the theories proposed in this paper were verified.

Predefined-Time Formation Control for Multiagent Systems-Based on Distributed Optimization

This article presents a solution to the leaderless formation control problem for first-order multiagent systems, which minimizes a global function composed of a sum of local strongly convex functions for each agent under weighted undirected graphs within a predefined time. The proposed distributed optimization process consists of two steps: 1) the controller initially leads each agent to the minimizer of its local function and 2) then guides all agents toward achieving leaderless formation and reaching the global function's minimizer. The proposed scheme requires fewer adjustable parameters than most existing methods in the literature without the need for auxiliary variables or time-variable gains. Additionally, one can consider highly nonlinear multivalued strongly convex cost functions, while the agents do not share the gradients and Hessians. Extensive simulations and comparisons with state-of-the-art algorithms demonstrate the effectiveness of our approach.