A distributed fixed-time optimization algorithm for multi-agent systems

TL;DR: A survey of fixed-time and prescribed-time consensus control in multiagent systems is presented in this paper , where the authors present a survey of recent trends and methodologies of consensus control.

TL;DR: The proposed control scheme is applied to solving an optimal formation control problem of general integrator chain multi-agent systems with strongly convex local cost functions, where the robots total relative weighted squared moving distance requires minimization.

TL;DR: In this paper , a distributed optimization algorithm involving two stages is designed, where the first stage is to make each agent converge to its own locally optimal state (the minimizer of local cost function) from any initial value in fixed time by designing distributed local optimization controllers.

TL;DR: In this paper , a robust continuous-time optimization algorithm for distributed problems is presented which guarantees fixed-time convergence, based on a Lyapunov function technique and applied to a class of problems with coupled local cost functions.

TL;DR: The authors' convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.

TL;DR: Two types of nonlinear control algorithms are presented for uncertain linear plants, stabilizing polynomial feedbacks that allow to adjust a guaranteed convergence time of system trajectories into a prespecified neighborhood of the origin independently on initial conditions.

TL;DR: A result relating regularity properties of a homogeneous function to its degree of homogeneity and the local behavior of the dilation near the origin makes it possible to extend previous results on homogeneous systems to the geometric framework.

TL;DR: This work develops and analyze distributed algorithms based on dual subgradient averaging and provides sharp bounds on their convergence rates as a function of the network size and topology, and shows that the number of iterations required by the algorithm scales inversely in the spectral gap of thenetwork.