Network semantics of dynamical systems

2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)

2015

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This paper demonstrates that the signal structure of an interconnected dynamical system, such as that represented by various dynamical graphical models, can be very different from the interconnection structure of its subsystems. These differences are revealed by considering the network semantics of each representation, characterized by the set of realizations consistent with each network structure. The reason these system networks can be so different is that subsystems never share hidden state, and thus subsystem structures partition the entire system state, while signal structures remain agnostic towards the presence of shared hidden state. Remaining agnostic towards the presence of shared hidden state make signal structures a weaker description of the underlying system structure, but it also gives signal structures a lower information cost for identification. Identifying a system's subsystem structure demands the identification of the correct partition of system states, which can be very expensive.

Section: Network Semanticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shared hidden state and network representations of interconnected dynamical systems

2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)

2015

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“…The dynamical structure function of a linear time invariant system is characterized by the following equation [5], 2) and the derivation of (Q, P ) from (A, B, C, D) is as follow:…”

Section: Dynamical Structure Functionsmentioning

confidence: 99%

Necessary and Sufficient Conditions on State Transformations That Preserve the Causal Structure of LTI Dynamical Networks

Leung

2018 IEEE Conference on Decision and Control (CDC)

2018

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“…It is precisely this particular type of constraints on the coprime factors of the controller that induces the distributed DRAFT implementation of resulted controllers as a network of linear time-invariant subsystems, such that the sub-controller on board each vehicle uses only information from its predecessor in the string. This approach to distributed controllers as linear dynamical networks hinges on the concept of dynamical structure functions, originally introduced in [47], [35] and further developed in [36], [37], [38], [39], [40], [41], [43], [44], [45].…”

Section: A Contributions Of This Papermentioning

confidence: 99%

Optimal Distributed Control for Platooning via Sparse Coprime Factorizations

Sabău

Oară

IEEE Trans. Automat. Contr.

et al. 2017

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We introduce a novel distributed control architecture for heterogeneous platoons of linear time-invariant autonomous vehicles. Our approach is based on a generalization of the concept of leader-follower controllers for which we provide a Youla-like parameterization, while the sparsity constraints are imposed on the controller's left coprime factors, outlying a new concept of structural constraints in distributed control. The proposed scheme is amenable to optimal controller design via norm based costs, it guarantees string stability and eliminates the accordion effect from the behavior of the platoon. We also introduce a synchronization mechanism for the exact compensation of the time delays induced by the wireless broadcasting of information.