Achieving distributed convex optimization within prescribed time for high-order nonlinear multiagent systems

TL;DR

This paper develops a framework for distributed convex optimization in nonlinear multi-agent systems that guarantees convergence within a prescribed time, even under disturbances and uncertainties, using cascade design and Lyapunov methods.

Contribution

It introduces a novel cascade design framework for prescribed-time convex optimization in nonlinear multi-agent systems, handling disturbances and uncertainties without high-order derivatives.

Findings

Proposed a cascade design framework for prescribed-time stabilization.

Successfully solved robust and adaptive DPTCO problems.

Verified effectiveness through numerical examples.

Abstract

In this paper, we address the distributed prescribed-time convex optimization (DPTCO) problem for a class of nonlinear multi-agent systems (MASs) under undirected connected graph. A cascade design framework is proposed such that the DPTCO implementation is divided into two parts: distributed optimal trajectory generator design and local reference trajectory tracking controller design. The DPTCO problem is then transformed into the prescribed-time stabilization problem of a cascaded system. Changing Lyapunov function method and time-varying state transformation method together with the sufficient conditions are proposed to prove the prescribed-time stabilization of the cascaded system as well as the uniform boundedness of internal signals in the closed-loop systems. The proposed framework is then utilized to solve robust DPTCO problem for a class of chain-integrator MASs with external disturbances by constructing a novel variables and exploiting the property of time-varying gains. The proposed framework is further utilized to solve the adaptive DPTCO problem for a class of strict-feedback MASs with parameter uncertainty, in which backstepping method with prescribed-time dynamic filter is adopted. The descending power state transformation is introduced to compensate the growth of increasing rate induced by the derivative of time-varying gains in recursive steps and the high-order derivative of local reference trajectory is not required. Finally, theoretical results are verified by two numerical examples.