This paper presents a new approach to distributed linear filtering and prediction. The problem under consideration consists of a random dynamical system observed by a multi-agent network of sensors where the network i...
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ISBN:
(纸本)9789082797091
This paper presents a new approach to distributed linear filtering and prediction. The problem under consideration consists of a random dynamical system observed by a multi-agent network of sensors where the network is sparse. Inspired by the consensus+innovations type of distributed estimation approaches, this paper proposes a novel algorithm that fuses the concepts of consensus and innovations. The paper introduces a definition of distributed observability, required by the proposed algorithm, which is a weaker assumption than that of global observability and connected network assumptions combined together. Following first principles, the optimal gain matrices are designed such that the mean-squared error of estimation is minimized at each agent and the distributed version of the algebraic Riccati equation is derived for computing the gains.
In this paper, we study the problem of path computation in multi-layer multi-switching networks. Compared to the standard shortest path problem, path computation in this context needs to take into account the heteroge...
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ISBN:
(纸本)9798350371000;9798350370997
In this paper, we study the problem of path computation in multi-layer multi-switching networks. Compared to the standard shortest path problem, path computation in this context needs to take into account the heterogeneous switching capabilities of nodes. We develop a routing algorithm by adapting the Floyd-Warshall algorithm to take into account the switching technology conversion. Our algorithm solves the all-pair min-cost continuous path problem by building routing tables for each node to allows hop-to-hop routing. We then extend our efforts by developing a distributed routing algorithm to construct the routing tables based on local information and interactions with direct neighbors. We complete our algorithmic analysis with extensive simulations to demonstrate the effectiveness of the developed routing algorithms.
We consider distributed online learning for joint regret with communication constraints. In this setting, there are multiple agents that are connected in a graph. Each round, an adversary first activates one of the ag...
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We consider distributed online learning for joint regret with communication constraints. In this setting, there are multiple agents that are connected in a graph. Each round, an adversary first activates one of the agents to issue a prediction and provides a corresponding gradient, and then the agents are allowed to send a b-bit message to their neighbors in the graph. All agents cooperate to control the joint regret, which is the sum of the losses of the activated agents minus the losses evaluated at the best fixed common comparator parameters u. We observe that it is suboptimal for agents to wait for gradients that take too long to arrive. Instead, the graph should be partitioned into local clusters that communicate among themselves. Our main result is a new method that can adapt to the optimal graph partition for the adversarial activations and gradients, where the graph partition is selected from a set of candidate partitions. A crucial building block along the way is a new algorithm for online convex optimization with delayed gradient information that is comparator-adaptive, meaning that its joint regret scales with the norm of the comparator parallel to u parallel to. We further provide near-optimal gradient compression schemes depending on the ratio of b and the dimension times the diameter of the graph.
In this paper, the emerging social cost minimization framework of second -order nonlinear systems over weight -unbalanced digraphs is studied. It provides a general framework containing several important classes of pr...
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In this paper, the emerging social cost minimization framework of second -order nonlinear systems over weight -unbalanced digraphs is studied. It provides a general framework containing several important classes of problems. Therein each local cost function depends on a global decision variable consisting of all the agents' decisions not limited to the form of aggregative terms. Under the partial decision and cost function information setting, distributed algorithms are designed based on the state feedback and left eigenvector estimation mechanism to overcome the challenges posed by second -order nonlinear systems and weightunbalanced digraphs. The asymptotic convergence of the algorithm is demonstrated via the Lyapunov stability theory. Finally, numerical simulations of the vehicle monitoring problem are provided to support the algorithm design.
This paper studies the constrained distributed resource allocation problems (DRAPs) of autonomous multi-agent systems (MASs). Unlike well-defined DRAPs, the disturbed second-order agents are taken into account. Moreov...
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This paper studies the constrained distributed resource allocation problems (DRAPs) of autonomous multi-agent systems (MASs). Unlike well-defined DRAPs, the disturbed second-order agents are taken into account. Moreover, all agents' decisions are subject to both coupling and local inequalities. Because of the coexistence of disturbed second-order nonlinear dynamics and inequality constraints, existing strategies are not applicable to tackle our problem. Also, they create challenges for the strategy design, because the inequality constraints must hold at the optimal allocation (OA), while these agents are not able to control their decisions directly. By gradient descent, state feedback and internal model (IM), we exploit a distributed strategy to control all agents to carry out the distributed resource allocation tasks (DRATs). Furthermore, the strategy is rigorously analyzed. Finally, our strategy is applied to the economic dispatch problems (EDPs). Under our approach, turbine-generators can autonomously achieve the optimal generation allocation by regulating their powers, in accordance with the load demand of smart grids.
The development of a scheduling strategy for an islanded microgrid (IMG) is critical for ensuring the system's stability and economic efficiency. Traditional scheduling strategies for IMGs predominantly utilize ce...
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The development of a scheduling strategy for an islanded microgrid (IMG) is critical for ensuring the system's stability and economic efficiency. Traditional scheduling strategies for IMGs predominantly utilize centralized management by the microgrid central controller (MGCC), which introduces a vulnerability to a single point of failure. To address this limitation, this paper presents a two-layer energy management strategy for IMGs based on the improved alternating direction method of multipliers (ADMM) and inverse reinforcement learning (IRL). First, the framework of the proposed strategy, comprising a scheduling layer and a real-time dispatch layer, is outlined. Next, the problem formulation of the scheduling layer is analyzed, and the proposed IRLbased management strategy for the energy storage system (ESS) is presented. Then, a distributed optimization algorithm based on the improved ADMM is proposed for the management of controllable distributed generators (CDGs) in the real-time dispatch layer. Lastly, the case study demonstrates the efficacy of the proposed strategy in diminishing MGCC dependency. The comparative analysis indicates that the proposed strategy outperforms existing scheduling strategies in terms of cost-effectiveness when the forecast error exceeds 3%. Moreover, in contrast to existing scheduling strategies, the proposed strategy mitigates the risk associated with a single point of failure.
This paper considers the distributed Nash equilibrium seeking problem for quadratic games in discrete-time systems with bounded control inputs. First, a saturation gradient algorithm is proposed to seek the Nash equil...
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This paper considers the distributed Nash equilibrium seeking problem for quadratic games in discrete-time systems with bounded control inputs. First, a saturation gradient algorithm is proposed to seek the Nash equilibrium without considering the limitations of communication. Then the case that players can only communicate with their neighbors is considered, a distributed Nash equilibrium seeking algorithm is designed where a consensus protocol is adapted for information sharing. In the proposed distributed algorithm, each player has an estimate on others' actions and the consensus of players' estimates is achieved. By Lyapunov stability theory for discrete-time systems, it is shown that the Nash equilibrium of the game is globally asymptotically stable under certain conditions. Moreover, distributed Nash equilibrium seeking problem in hybrid systems with bounded control inputs is solved. Finally, two numerical examples are presented to verify the effectiveness of the proposed algorithms.
Leader Election is an important primitive for programmable matter, since it is often an intermediate step for the solution of more complex problems. Although the leader election problem itself is well studied even in ...
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ISBN:
(纸本)9783031606021;9783031606038
Leader Election is an important primitive for programmable matter, since it is often an intermediate step for the solution of more complex problems. Although the leader election problem itself is well studied even in the specific context of programmable matter systems, research on fault tolerant approaches is more limited. We consider the problem in the previously studied Amoebot model on a triangular grid, when the configuration is connected but contains nodes the particles cannot move to (e.g., obstacles). We assume that particles agree on a common direction (i.e., the horizontal axis) but do not have chirality (i.e., they do not agree on the other two directions of the triangular grid). We begin by showing that an election algorithm with explicit termination is not possible in this case, but we provide an implicitly terminating algorithm that elects a unique leader without requiring any movement. These results are in contrast to those in the more common model with chirality but no agreement on directions, where explicit termination is always possible but the number of elected leaders depends on the symmetry of the initial configuration. Solving the problem under the assumption of one common direction allows for a unique leader to be elected in a stationary and deterministic way, which until now was only possible for simply connected configurations under a sequential scheduler.
In a reconfiguration problem, given a problem and two feasible solutions of the problem, the task is to find a sequence of transformations to reach from one solution to the another such that every intermediate state i...
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ISBN:
(纸本)9783031210167;9783031210174
In a reconfiguration problem, given a problem and two feasible solutions of the problem, the task is to find a sequence of transformations to reach from one solution to the another such that every intermediate state is also a feasible solution to the problem. In this paper, we study the distributed spanning tree reconfiguration problem and we define a new reconfiguration step, called k-simultaneous add and delete, in which every node is allowed to add at most k edges and delete at most k edges such that multiple nodes do not add or delete the same edge. We first show that, if the two input spanning trees are rooted then we can transform one into another in one round using a single 1-simultaneous add and delete step in the CONGEST model. Therefore, we focus our attention towards unrooted spanning trees and show that transforming an unrooted spanning tree into another using a single 1-simultaneous add and delete step requires Omega(n) rounds in the LOCAL model. We additionally show that transforming an unrooted spanning tree into another using a single 2-simultaneous add and delete step can be done in O(log n) rounds in the CONGEST model.
We present a random finite set-based method for achieving comprehensive situation awareness by each vehicle in a distributed vehicle network. Our solution is designed for labeled multi-Bernoulli filters running in eac...
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ISBN:
(数字)9781665452489
ISBN:
(纸本)9781665452489
We present a random finite set-based method for achieving comprehensive situation awareness by each vehicle in a distributed vehicle network. Our solution is designed for labeled multi-Bernoulli filters running in each vehicle. It involves complementary fusion of sensor information locally running through consensus iterations. We introduce a novel label merging algorithm to eliminate double counting. We also extend the label space to incorporate sensor identities. This helps to overcome label inconsistencies. We show that the proposed algorithm is able to outperform the standard LMB filter using a distributed complementary approach with limited fields of view.
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