In this paper, we present a distributed estimation setup where autonomous agents estimate their states from coupled measurements, that is, measurements that depend on multiple agents. For instance, in the case of mult...
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In this paper, we present a distributed estimation setup where autonomous agents estimate their states from coupled measurements, that is, measurements that depend on multiple agents. For instance, in the case of multiagent systems, where only relative measurements are available, this is of high relevance. This paper proposes a distributed observer design solution, which is scalable with respect to the number of agents. This distributed observer is then used for the design of a distributed observer-based output synchronization control algorithm. Robust performance against exogenous and measurement disturbances can be guaranteed for both the estimation error and synchronization error.
In distributedcontrol systems with shared resources, participating agents can improve the overall performance of the system by sharing data about their personal preferences. In this paper, we formulate and study a na...
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In distributedcontrol systems with shared resources, participating agents can improve the overall performance of the system by sharing data about their personal preferences. In this paper, we formulate and study a natural tradeoff arising in these problems between the privacy of the agent's data and the performance of the control system. We formalize privacy in terms of differential privacy of agents' preference vectors. The overall control system consists of N agents with linear discrete-time coupled dynamics, each controlled to track its preference vector. Performance of the system is measured by the mean squared tracking error. We present a mechanism that achieves differential privacy by adding Laplace noise to the shared information in a way that depends on the sensitivity of the control system to the private data. We show that for stable systems the performance cost of using this type of privacy preserving mechanism grows as O(T-3/N epsilon(2)), where T is the time horizon and epsilon is the privacy parameter. For unstable systems, the cost grows exponentially with time. From an estimation point of view, we establish a lower-bound for the entropy of any unbiased estimator of the private data from any noise-adding mechanism that gives epsilon-differential privacy. We show that the mechanism achieving this lower-bound is a randomized mechanism that also uses Laplace noise.
This paper investigates the use of event-based communication in a distributed model predictive control (DMPC) scheme for linear subsystems interconnected by dynamics and costs. In the proposed DMPC scheme, all subsyst...
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This paper investigates the use of event-based communication in a distributed model predictive control (DMPC) scheme for linear subsystems interconnected by dynamics and costs. In the proposed DMPC scheme, all subsystems optimize their local input sequences in parallel, and local iterations are performed to update the global input sequence. To reduce the load on the communication network, we propose an event-based communication protocol, in which local information is only communicated if doing so results in a sufficient improvement of the overall control performance. Based on the event generator and a distributed stopping criterion, we first establish that the scheme terminates after a finite number of iterations, and we provide bounds on the suboptimality of the solution. It is shown that the suboptimality of the scheme can be made arbitrarily small by choosing an appropriate threshold. Subsequently, a bound on the convergence rate is established. Based on this bound, parameters used in the scheme are optimized for fast convergence. Finally, the stability properties of the proposed DMPC scheme are analyzed for the case, with and without terminal constraint. We illustrate our analysis by numerical examples and compare the load on the communication network and the suboptimality for event-based communication and full communication in every iteration.
This paper studies distributed strategies for average-consensus of arbitrary vectors in multiagent systems, when the interagent information exchange is corrupted by the agents' states within the same network. In p...
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This paper studies distributed strategies for average-consensus of arbitrary vectors in multiagent systems, when the interagent information exchange is corrupted by the agents' states within the same network. In particular, each neighboring state received by an agent has an additive component that consists of projections of the states at other agents;the agents corrupting this exchange are unknown to the receiving agent and may also change over time. We model such in-network disturbance with a dynamic disturbance graph over the agents, in addition to the static graph over which consensus is implemented. The problem in its full generality is quite challenging and in an attempt to simplify, we assume two particular disturbance cases: 1) sender based and 2) receiver based. In the former case, we assume that the (null spaces of the) projection subspaces are only known at the senders;while in the latter case, we assume this knowledge only at the receivers. We provide a concrete example of static, flat-fading multiple-input multiple-output channels to support this disturbance model. In the aforementioned context, we cast an algebraic structure over the disturbance subspaces and show that the average is reachable in a subspace whose dimension is complementary to the maximal dimension of the disturbance subspaces. To develop the results, we introduce the notion of information alignment to align the intended message to the null space of the unintended disturbance. We derive the conditions under which this alignment is invertible, that is, the intended message can be recovered. A major contribution of this work is to show that local protocols exist for (subspace) consensus even when the disturbance over the network spans the entire vector space.
We present an adaptive algorithm that guarantees synchronization in diffusively coupled systems. We first consider compartmental systems of ODEs where variables in each compartment are interconnected through diffusion...
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We present an adaptive algorithm that guarantees synchronization in diffusively coupled systems. We first consider compartmental systems of ODEs where variables in each compartment are interconnected through diffusion terms with like variables in other compartments. Each set of variables may have its own weighted undirected graph describing the topology of the interconnection between compartments. The link weights are updated adaptively according to the magnitude of the difference between neighboring agents connected by each link. We show that an incremental passivity property is fundamental in guaranteeing output synchronization. We next consider reaction-diffusion PDEs with Neumann boundary conditions and derive an analogous algorithm guaranteeing spatial homogenization of the solutions. We provide several numerical examples demonstrating the results.
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