This paper addresses the distributed computation of the linear algebraic equations (LAEs) from a control perspective. The fundamental methodology involves interpreting the unknown variables of the LAE as the states of...
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ISBN:
(数字)9798331504755
ISBN:
(纸本)9798331504762
This paper addresses the distributed computation of the linear algebraic equations (LAEs) from a control perspective. The fundamental methodology involves interpreting the unknown variables of the LAE as the states of a related dynamic system and harnessing the full potential of distributed state observers, within the context of multi-agent systems. The proposed distributed algorithm leverages the combination of observer-based design and consensus-based design principles. It is shown that regardless of whether the LAE of interest has a unique solution or multiple solutions, the presented distributed algorithm can converge exponentially fast to the solution, independent of the choice of initial values. This control-oriented approach demonstrates the idea of control design by effectively leveraging control tools, specifically the distributed state observer, to address the mathematical problem of equation-solving. Finally, simulation examples are presented to demonstrate the efficacy of the proposed methods.
In this paper, we propose a multi-tiered framework for controlling distributed energy resources (DERs) such as elastic and non-elastic loads, electric vehicles (EV s), and Battery Energy Storage Systems (BESS). These ...
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ISBN:
(数字)9798331524852
ISBN:
(纸本)9798331536411
In this paper, we propose a multi-tiered framework for controlling distributed energy resources (DERs) such as elastic and non-elastic loads, electric vehicles (EV s), and Battery Energy Storage Systems (BESS). These resources are organized into clusters. By distributing the control algorithm across these tiers, DERs optimize local operations and enhance system resilience. Local agents (LIAs) at the lowest tier optimize individual units. Cluster agents (CI As) at the second-tier aggregate cluster loads. The orchestrator agent (01 A) at the highest tier communicates with utilities or ISO/TSOs. Each LI A periodically solves a stochastic mixed-integer linear programming (MILP) problem to minimize costs and flatten the load profile. LI A results are aggregated by CI As to calculate flexible capacities. The 01 A aggregates this data to identify operational violations with the network operator, distributing necessary corrections among clusters.
In this article, we focus on solving a class of distributed optimization problems involving $n$ agents with the local objective function at every agent $i$ given by the difference of two convex functions $f_{i}$ ...
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ISBN:
(数字)9798331541033
ISBN:
(纸本)9798331541040
In this article, we focus on solving a class of distributed optimization problems involving
$n$
agents with the local objective function at every agent
$i$
given by the difference of two convex functions
$f_{i}$
and
$g_{i}$
(difference-of-convex (DC) form), where
$f_{i}$
and
$g_{i}$
are potentially nonsmooth. The agents communicate via a directed graph containing
$n$
nodes. We create smooth approximations of the functions
$f_{i}$
and
$g_{i}$
and develop a distributed algorithm utilizing the gradients of the smooth surrogates and a finite-time approximate consensus protocol. We term this algorithm as DDC-Consensus. The developed DDC-Consensus algorithm allows for non-symmetric directed graph topologies and can be synthesized distributively. We establish that the DDC-Consensus algorithm converges to a stationary point of the nonconvex distributed optimization problem. The performance of the DDC-Consensus algorithm is evaluated via a simulation study to solve a nonconvex DC-regularized distributed least squares problem. The numerical results corroborate the efficacy of the proposed algorithm.
In this paper, we investigate the problem of distributed load balancing under network capacity constraints, where the participating agents cooperate with the aim of jointly minimizing both the workload disparity among...
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In this paper, we investigate the problem of distributed load balancing under network capacity constraints, where the participating agents cooperate with the aim of jointly minimizing both the workload disparity among them as well as the overall workload transfer. Classical approaches for asymptotic convergence to the global optimum in a distributed fashion typically assume timely exchange of information between neighboring agents of a given multi-agent system. This assumption is not necessarily valid in practical settings due to non-commensurate (heterogeneous) communication and processing delays that might affect transmissions at different times. More specifically, we consider what effect multiple heterogeneous time-varying delays, among the agents can have on the distributed load balancing problem. We show that the distributed load balancing problem under bounded heterogeneous time-varying delays is globally asymptotically stable, but the rate of convergence is affected. Bounds on the convergence rate are provided with respect to an upper bound on the delays. Simulation examples are provided to show the validity and performance of our theoretical results.
This paper studies N-cluster games with secondorder dynamics, wherein the players’ decisions are restricted by local set constraints and nonlinear coupled inequality constraints. The presence of second-order dynamics...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper studies N-cluster games with secondorder dynamics, wherein the players’ decisions are restricted by local set constraints and nonlinear coupled inequality constraints. The presence of second-order dynamics coupled with constraints leads to difficulties in the design and analysis of generalized Nash equilibrium (GNE) seeking algorithms, since it may be impossible to directly determine the decisions of players based on their control inputs. To facilitate the autonomous execution of N-cluster game tasks through secondorder players, by employing state feedback, projection, primaldual, dynamic average consensus, and passivity methods, a distributed algorithm is proposed to find the variational GNE of the studied games, under which the players’ decisions can satisfy the set constraints all the time. Additionally, the algorithm’s convergence is rigorously analyzed, and its efficacy is validated by a simulation example.
In this paper, we introduce a comprehensive framework called edge agreement, where agents collaboratively strive to reach an agreement on non-linear edge constraints de-fined for each of their neighbors on every edge....
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
In this paper, we introduce a comprehensive framework called edge agreement, where agents collaboratively strive to reach an agreement on non-linear edge constraints de-fined for each of their neighbors on every edge. This provides a novel perspective on the traditional ‘global consensus' problem, extending the goal beyond reaching the same value to achieving agreement over the whole multi-agent team. We present a dis-tributed algorithm for achieving edge agreement, along with a convergence guarantee based on certain assumptions. Addition-ally, we illustrate how this framework encompasses a variety of cooperation problems, including global consensus, recent results on edge agreement with linear constraints, distributed solutions to linear algebraic equations, and formation control (distance and displacement-based) as special cases. Finally, to validate and demonstrate the significance of this framework, we provide numerical simulations for all the mentioned applications.
Buildings with shared spaces such as corporate office buildings, university dorms, etc. involve multiple occupants who are likely to have different temperature preferences. The building temperature control system need...
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ISBN:
(纸本)9781479917730
Buildings with shared spaces such as corporate office buildings, university dorms, etc. involve multiple occupants who are likely to have different temperature preferences. The building temperature control system needs to balance the desire of all users, as well as take the building energy cost into account. Centralized temperature management is challenging as the use comfort function is held privately and not centrally known. This paper proposes a distributed temperature control algorithm which ensures that a consensus is attained among all occupants, irrespective of their temperature preferences. Occupants are only assumed to be rational, in that they choose their own temperature set-points to minimize a combination of their individual energy cost and discomfort. We establish the convergence of the proposed algorithm to the optimal temperature set-point that minimizes the sum of the energy cost and the aggregate discomfort of all occupants. Simulations with realistic parameter settings illustrate the performance of the algorithm and provide insights on the dynamics of the system with a mobile user population.
In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collaboratively optimize the sum of local objective functions through their own continuous and/or discrete decision variable...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collaboratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting Mixed-Integer Nonlinear Programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in Mixed-Integer Linear Programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations.
This paper focuses on the problem of distributed leader-follower consensus in multi-agent systems in which some agents are subject to adversarial attacks. We develop a resilient distributed leader-follower control str...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper focuses on the problem of distributed leader-follower consensus in multi-agent systems in which some agents are subject to adversarial attacks. We develop a resilient distributed leader-follower control strategy subject to integrity attacks, where agents’ updates of their states can be compromised by injecting false signals to control inputs. Under such a threat model, we design a resilient distributed leader-follower framework for agents with continuous-time dynamics to resiliently track a reference state propagated by a leader. In the design of the resilient framework, projection-based operators are used as dynamic controllers to estimate the dynamics of uncertainties on the control inputs of each agent. By use of the properties of projection operators and Lyapunov stability theory, the uniform ultimate boundedness of the closed-loop multi-agent system in the presence of integrity attacks is guaranteed. The proposed resilient distributed scheme does not impose any limitations on the maximum tolerable number of cyberattacks and does not require high network connectivity. The effectiveness of the proposed resilient distributed consensus algorithm is verified by a numerical example.
In this article, we present a novel distributed algorithm for prescribed-time convergence acceleration with time rescaling. Two novel prescribed-time acceleration algorithms are designed to tackle distributed unconstr...
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ISBN:
(数字)9798331508661
ISBN:
(纸本)9798331508678
In this article, we present a novel distributed algorithm for prescribed-time convergence acceleration with time rescaling. Two novel prescribed-time acceleration algorithms are designed to tackle distributed unconstrained optimization problems and distributed optimization problems with constraints, effectively mirroring centralized optimization tasks within the framework of a closed convex set. We introduce several pivotal theorems, and the convergence of these algorithms are rigorously demonstrated through the application of Lyapunov functions. Finally, some comprehensive numerical simulations are conducted to validate the practicality and robustness of our theoretical findings.
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