基于Bandit反馈的自适应量化分布式在线镜像下降算法

多智能体系统的在线分布式优化常用于处理动态环境下的优化问题, 节点间需要实时传输数据流. 在很多情况下, 各节点无法获取个体目标函数的全部信息(包括梯度信息), 并且节点间信息传输存在一定的通信约束. 考虑到非欧投影意义下的镜像下降算法在处理高维数据和大规模在线学习上的优势, 本文使用个体目标函数在两点处的函数值信息对缺失的梯度信息进行估计, 并且根据镜像下降算法的性质设计自适应量化器, 提出基于Bandit反馈的自适应量化分布式在线镜像下降算法. 然后分析了量化误差界和Regret界的关系, 适当选择参数可得所提算法的Regret界为O(√T). 最后, 通过数值仿真验证了算法和理论结果的有效性     

一阶多智能体受扰系统的自抗扰分布式优化算法

研究一类具有未知外部干扰的一阶多智能体系统的分布式优化问题.在分布式优化任务中,每个智能体只被容许利用自己的局部目标函数和邻居的状态信息,设计一个分布式优化算法,使全局目标函数取得最小值,其中全局目标函数是所有局部目标函数之和.针对该问题,首先提出由扩张状态观测器和优化算法组成的自抗扰分布式优化算法.其次,在Lyapu...     

分布式约束优化

多Agent系统由于拥有智能性、自主性以及协同性等一系列的特性受到人们广泛的关注.分布式约束优化是协调多个Agent解决分布问题的有效技术,目前是多Agent领域的研究热点.本文将首先介绍分布式约束优化问题的基本概念和框架结构,总结现有的解决该问题的主要算法.并通过效率、性能、隐私等各方面对这些算法进行全面的比较与分析,然后介绍分布式约束优化问题的一些典型应用,最后还将对分布式约束优化问题及其算法未来的研究发展方向进行论述.     

基于分布式深度强化学习的微电网实时优化调度

随着海量新能源接入到微电网中, 微电网系统模型的参数空间成倍增长, 其能量优化调度的计算难度不断上升. 同时, 新能源电源出力的不确定性也给微电网的优化调度带来巨大挑战. 针对上述问题, 本文提出了一种基于分布式深度强化学习的微电网实时优化调度策略. 首先, 在分布式的架构下, 将主电网和每个分布式电源看作独立智能体. 其次, 各智能体拥有一个本地学习模型, 并根据本地数据分别建立状态和动作空间, 设计一个包含发电成本、交易电价、电源使用寿命等多目标优化的奖励函数及其约束条件. 最后, 各智能体通过与环境交互来寻求本地最优策略, 同时智能体之间相互学习价值网络参数, 优化本地动作选择, 最终实现最小化微电网系统运行成本的目标. 仿真结果表明, 与深度确定性策略梯度算法(Deep Deterministic Policy Gradient, DDPG)相比, 本方法在保证系统稳定以及求解精度的前提下, 训练速度提高了17.6%, 成本函数值降低了67%, 实现了微电网实时优化调度.     

具有外部干扰的二阶多自主体系统的协同运动

为解决多自主体系统在群集运动过程受到外部干扰影响的问题,本文研究了具有外部干扰的二阶多自主体系统的分布式协同控制.本文中的外部干扰包括匹配干扰和不匹配干扰,针对系统中的匹配干扰,设计了状态观测器和干扰观测器,对系统的未知状态和干扰进行估计,并且构造了基于干扰观测器的多自主体协同控制算法.对于系统中的不匹配干扰,设计了与匹配干扰不同的干扰观测器,构造了基于主动抗干扰观测器的协同控制算法.运用矩阵论和现代控制理论等方法,研究了基于干扰观测器的二阶多自主体系统的协同控制.应用计算机仿真分别验证在多自主体系统具有匹配干扰和不匹配干扰的情况下结论的有效性,仿真结果表明,本文所设计的多自主体协同控制算法可以使跟随者最终都收敛到领导者的状态,实现了具有匹配干扰和不匹配干扰的二阶多自主体系统的状态一致性.     

基于LQR的耦合动态互联系统分布式协作 负载均衡优化控制

针对一类非等同非线性耦合互联系统,提出分布式协作负载均衡优化控制方法.将子系统间的通信联系建模成有向图,借助输入输出反馈线性化技术,将耦合互联系统的分布式负载均衡控制设计问题转化为广义线性多智能体系统的同步跟踪问题;基于最近邻原则和LQR方法,设计增益可调的分布式协作负载均衡优化控制律,耦合强度依赖于通信拓扑,控制增益依赖于子系统模型;借助矩阵变换方法,整个闭环系统的渐近稳定性可以解耦成每个子系统的稳定性,在假定通信拓扑只含有生成树的条件下,借助李亚谱诺夫函数,可证明整个闭环系统是稳定的,且通过调节控制增益,可以得到期望的响应速度.仿真结果验证了所提出控制方法的有效性及可行性.     

矩阵方程的分布式求解算法研究概述

近年来,随着大规模网络的兴起和分布式优化理论的广泛应用,矩阵方程的分布式求解算法研究也受到了越来越多的重视.矩阵方程的计算求解在理论和工程领域都有着重要的意义.在多智能体网络下的分布式计算问题中,矩阵方程中的数据信息按照各种方式进行划分,单个智能体只能够获取其中的一份数据,然后通过与其邻居智能体进行信息交互,最终合作求解出不同类型的符合方程要求的解.本文集中讨论了近几年来针对线性代数方程、几类不带约束和带约束线性矩阵方程、以及其他矩阵相关的分布式计算和求解问题,介绍了投影一致方法、转化成分布式优化问题再求解的方法、以及针对特殊矩阵如稀疏矩阵的信息传递方法等分布式算法设计方法.最后,简要总结全文以及对分布式矩阵计算方向的研究进行了展望.     

网络化分布式凸优化算法研究进展

分布式优化是指利用网络化多自主体之间的协作来求解的一类优化问题,其在大规模数值计算、机器学习、资源分配、传感器网络等方面具有重要的研究意义和应用价值.自主体之间的协作通常基于代数图来描述,且图的结构对分布式优化算法的设计与性能有显著影响.本文针对凸优化问题,基于平衡图和非平衡图的情形,简要讨论了分布式优化算法的最新研究进展,并对今后的发展趋势和应用进行展望.     

分布式事件触发多自主体领导跟随一致性研究

为了提高系统的通信效率和能源利用率,减少多自主体系统硬件资源的浪费,提出了只需要自主体自身及其最近邻居节点信息的分布式事件触发控制算法。研究了带有动态领导者的二阶多自主体系统领导跟随一致性问题。应用矩阵论和现代控制理论研究了在分布式事件触发机制下的二阶系统,得到了基于事件触发机制的多自主体系统协同运动的收敛条件。通过理论分析与计算表明,在此控制协议下不会存在芝诺行为,并且多自主体系统可以实现领导跟随一致性。最后,应用计算机仿真验证了本文所提控制协议的可靠性。     

基于梯度估计的多智能体系统有限时间分布式优化

现有多智能体系统分布式优化算法大多具有渐近收敛速度,且要求系统的网络拓扑图为无向图或有向平衡图,在实际应用中具有一定的保守性.本文研究了具有强连通拓扑的多智能体系统有限时间分布式优化问题.首先,基于非光滑分析和Lyapunov稳定性理论设计了一个有限时间分布式梯度估计器.然后,基于该梯度估计器提出了一种适用于强连通有向图的有限时间分布式优化算法,实现了多智能体系统中智能体的状态在有限时间内一致收敛到全局最优状态值.与现有的有限时间分布式优化算法相比,新提出的有限时间优化算法适用于具有强连通拓扑的多智能体系统,放宽了系统对网络拓扑结构的要求.此外,本文基于Nussbaum函数方法对上述优化算法进行了拓展解决了含有未知高频增益符号的多智能体系统分布式优化问题.最后,通过仿真实例对提出的分布式优化算法的有效性进行了验证.     

Cooperative output regulation of heterogeneous multi-agent systems based on passivity

This paper investigates the problem of cooperative output regulation of heterogeneous linear multi-agent systems. A passive framework is presented for the stabilisation analysis of cooperative output regulation, which can overcome the difficulty caused by the fact that the global dynamics of heterogeneous multi-agent systems depends on the global communication structure. An adaptive distributed observer is proposed to estimate the state of the exosystem, and the proposed distributed observer is independent of any global information of the communication graph. Based on passivity design and adaptive distributed observer, both a distributed state feedback and a distributed output feedback protocol are designed for output synchronisation of heterogeneous multi-agent systems. The gain matrices of the distributed protocols and observers are obtained by a Riccati equation design approach. Furthermore, sufficient local conditions for solving the problem of cooperative output regulation of heterogeneous multi-agent systems are presented. Finally, numerical simulation results are given to illustrate the effectiveness of the proposed distributed control schemes.     

Output feedback distributed optimization algorithms of second-order Lipschitz nonlinear multi-agent systems

This paper introduces output feedback distributed optimization algorithms designed specifically for second-order nonlinear multi-agent systems. The agents are allowed to have heterogeneous dynamics, characterized by distinct nonlinearities, as long as they satisfy the Lipschitz continuity condition. For the case with unknown states, nonlinear state observers are designed first for each agent to reconstruct agents' unknown states. It is proven that the agents' unknown states are estimated accurately by the developed state observers. Then, based on the agents' state estimates and the gradient of each agent local cost function, a kind of output feedback distributed optimization algorithms are proposed for the considered multi-agent systems. Under the proposed distributed optimization algorithms, all the agents' outputs asymptotically approach the minimizer of the global cost function which is the sum of all the local cost functions. By using Lyapunov stability theory, convex analysis, and input-to-state stability theory, the asymptotical convergence of the output feedback distributed optimization closed-loop system is proven. Simulations are conducted to validate the efficacy of the proposed algorithms.     

Cooperative output regulation with application to multi-agent consensus under switching network

In this paper, we consider the cooperative output regulation of linear multi-agent systems under switching network. The problem can be viewed as a generalization of the leader-following consensus problem of multi-agent systems. Due to the limited information exchanges of different subsystems, the problem cannot be solved by the decentralized approach and is not allowed to be solved by the centralized control. By devising a distributed observer network, we can solve the problem by both dynamic state feedback control and dynamic measurement output feedback control. As an application of our main result, we show that a special case of our results leads to the solution of the leader-following consensus problem of linear multi-agent systems.     

A survey of distributed optimization

In distributed optimization of multi-agent systems, agents cooperate to minimize a global function which is a sum of local objective functions. Motivated by applications including power systems, sensor networks, smart buildings, and smart manufacturing, various distributed optimization algorithms have been developed. In these algorithms, each agent performs local computation based on its own information and information received from its neighboring agents through the underlying communication network, so that the optimization problem can be solved in a distributed manner. This survey paper aims to offer a detailed overview of existing distributed optimization algorithms and their applications in power systems. More specifically, we first review discrete-time and continuous-time distributed optimization algorithms for undirected graphs. We then discuss how to extend these algorithms in various directions to handle more realistic scenarios. Finally, we focus on the application of distributed optimization in the optimal coordination of distributed energy resources.     

多智能体系统动态协调与分布式控制设计

多智能体系统的主要研究目的在于探索由个体之间的相互作用所产生的群体协调现象的内在机制和原理,而控制或反馈在多智能体协调运动中起着至关重要的作用．本文集中讨论了多智能体协调研究中的几个新兴的基本问题,包括输出调节、集合协调和覆盖．文中着重介绍了分布式估计和内模原理两种多智能体系统分布式输出调节方法及相关的研究进展：关于多智能体系统的目标集合协调,本文从集合聚集和集合优化两方面做了详尽论述：多智能体覆盖有多种分类方式,从覆盖对象的特征出发可将其划分为区域覆盖、边界覆盖和动态目标覆盖3种类型,并对它们的研究背景和最新成果予以介绍．另外文章还对多智能体系统协调控制的理论和应用研究进行了展望．     

基于切换拓扑的多智能体协作控制研究综述

多智能体系统一直是众多学科领域研究的主要研究对象,基于切换拓扑的多智能体协作控制理论研究作为多智能体系统研究的重要部分,一直是近年来的热点。为了推进基于切换拓扑的多智能体协作控制理论研究,在广泛调研现有文献和最新成果的基础上,从一致性问题、分布式优化问题和分布式估计问题三个方面对该领域的发展现状进行了总结;探讨了诸如一致性协议的设计、一致性协议的性能分析方法及其优缺点、分布式优化的实现方式和分布式估计的实际应用。最后指出当前该领域尚未解决的问题和未来的研究方向。     

Distributed adaptive consensus for linear multi-agent systems with quantised information

This paper considers the distributed adaptive consensus problem for linear multi-agent systems with quantised relative information. By using a lemma in algebraic graph theory and introducing a projection operator in adaptive law, a novel distributed adaptive state feedback controller is designed with quantised relative state information. It is shown that the practical consensus for multi-agent systems with a uniform quantiser is achieved via the Lyapunov theory and the non-smooth analysis. In contrast with the existing quantised controllers, which rely on the minimum nonzero eigenvalue of the Laplacian matrix, the developed controller is only dependent on the number of nodes. Furthermore, a dynamic output feedback controller based on quantised relative output information is proposed. Finally, a simulation example is given to illustrate the effectiveness of the proposed control scheme.     

Random gradient-free method for online distributed optimization with strongly pseudoconvex cost functions

This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state information with its current neighbors through a time-varying digraph. In addition, the agents do not have access to the information about the current cost functions until decisions are made. Different from most existing works on online distributed optimization, here we consider the case where the cost functions are strongly pseudoconvex and real gradients of the cost functions are not available. To handle this problem, a random gradient-free online distributed algorithm involving the multi-point gradient estimator is proposed. Of particular interest is that under the proposed algorithm, each agent only uses the estimation information of gradients instead of the real gradient information to make decisions. The dynamic regret is employed to measure the proposed algorithm. We prove that if the cumulative deviation of the minimizer sequence grows within a certain rate, then the expectation of dynamic regret increases sublinearly. Finally, a simulation example is given to corroborate the validity of our results.     

Cooperative global output regulation of heterogeneous second-order nonlinear uncertain multi-agent systems

In this paper, we study the cooperative global output regulation problem for a class of heterogeneous second order nonlinear uncertain multi-agent systems. We first introduce a type of distributed internal model that converts the cooperative global output regulation problem into the global robust stabilization problem of the so-called augmented multi-agent system. Then we further globally stabilize this augmented multi-agent system via a distributed state feedback control law, thus leading to the solution of the original problem. A special case of our result leads to the solution of the global leader-following consensus problem for the second order nonlinear multi-agent systems without satisfying the global Lipschitz condition.