Communication strategies to ensure generic networked observability in multi-agent systems

Doostmohammadian, Mohammadreza; Khan, Usman A.

doi:10.1109/acssc.2011.6190346

Cited by 21 publications

(44 citation statements)

References 17 publications

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“…1C). By flipping the direction of each edge, the procedure recovers the system digraph encountered in structured systems theory (11)(12)(13). ii) Strongly connected component (SCC) decomposition: We decompose the inference diagram into a unique set of maximal SCCs, which are the largest subgraphs chosen such that there is a directed path from each node to every other node in the subgraph (14).…”

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confidence: 99%

Observability of complex systems

Liu

Slotine

Barabási

2013

Proc. Natl. Acad. Sci. U.S.A.

448

452

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A quantitative description of a complex system is inherently limited by our ability to estimate the system's internal state from experimentally accessible outputs. Although the simultaneous measurement of all internal variables, like all metabolite concentrations in a cell, offers a complete description of a system's state, in practice experimental access is limited to only a subset of variables, or sensors. A system is called observable if we can reconstruct the system's complete internal state from its outputs. Here, we adopt a graphical approach derived from the dynamical laws that govern a system to determine the sensors that are necessary to reconstruct the full internal state of a complex system. We apply this approach to biochemical reaction systems, finding that the identified sensors are not only necessary but also sufficient for observability. The developed approach can also identify the optimal sensors for target or partial observability, helping us reconstruct selected state variables from appropriately chosen outputs, a prerequisite for optimal biomarker design. Given the fundamental role observability plays in complex systems, these results offer avenues to systematically explore the dynamics of a wide range of natural, technological and socioeconomic systems.algebraic observability | biochemical reactions | control theory T he internal variables of a complex system are rarely independent of each other, as the interactions between the system's components induce systematic interdependencies between them. Hence, a well-selected subset of variables can contain sufficient information about the rest of the variables, allowing us to reconstruct the system's complete internal state, making the system observable. To address observability in quantitative terms, we focus on systems whose dynamics can be described by the generic state-space form _ xðtÞ = fðt; xðtÞ; uðtÞÞ;[1]where xðtÞ ∈ R N represents the complete internal state of the system (e.g., the concentrations of all metabolites in a cell), and the input vector uðtÞ ∈ R K captures the influence of the environment. Observing the system means that we monitor a set of variables yðtÞ ∈ R M that depend on the time t, the system's internal state xðtÞ, and the external input uðtÞ, yðtÞ = hðt; xðtÞ; uðtÞÞ:[2]Observability requires us to establish a relationship between the outputs yðtÞ, the state vector xðtÞ, and the inputs uðtÞ in a manner that we can uniquely infer the system's complete initial state xð0Þ. The observability criteria can be formulated algebraically for dynamical systems consisting of polynomial or rational expressions (1, 2) stating that [1] is observable if the Jacobian matrix J = ½J ij NM × N has full rank, rank J = N;[3]where

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mentioning

confidence: 99%

Observability of complex systems

Liu

Slotine

Barabási

2013

Proc. Natl. Acad. Sci. U.S.A.

448

452

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“…To this end, we resort to structural properties of the system that rely on zeros and non-zeros of the system matrices and provide generic observability results that are true for almost all choices of system parameters given that the underlying structure is 1 Notice that, in this theorem being strongly connected is a sufficient condition. For certain cases less connectivity in communication network is required (see [17]). not violated.…”

Section: Discussionmentioning

confidence: 99%

A sensor placement and network design paradigm for future smart grids

Khan

Doostmohammadian

2011

2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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In this paper, we propose a method for sensor placement and communication network design for the purpose of distributed estimation in future smart grids. We use generic observability from structured systems theory to devise sensor placement strategies that are independent of the actual parameter values and are only a function of the underlying structure, i.e., the zero and non-zero pattern, of the physical network. We then use these results to design communication networks among the sensors that ensure observability of the distributed observers formulated on the sensor measurements.

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“…In the above formulation, A is the dynamical system matrix, k is the time-step, y k i is the measurement of agent i at time-step k, and v k and r k i are the noise terms. Following the distributed estimation protocol in [10], [35]- [38], the following protocol based on the consensus-update law (3) is considered:…”

Section: B Distributed Estimationmentioning

confidence: 99%

Single-Bit Consensus With Finite-Time Convergence: Theory and Applications

Doostmohammadian

2020

IEEE Trans. Aerosp. Electron. Syst.

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In this brief paper, a new consensus protocol based on the sign of innovations is proposed. Based on this protocol each agent only requires single-bit of information about its relative state to its neighboring agents. This is significant in real-time applications, since it requires less computation and/or communication load on agents. Using Lyapunov stability theorem the convergence is proved for networks having a spanning tree. Further, the convergence is shown to be in finite-time, which is significant as compared to most asymptotic protocols in the literature. Time-variant network topologies are also considered in this paper, and final consensus value is derived for undirected networks. Applications of the proposed consensus protocol in (i) 2D/3D rendezvous task, (ii) distributed estimation, (iii) distributed optimization, and (iv) formation control are considered and significance of applying this protocol is discussed. Numerical simulations are provided to compare the protocol with the existing protocols in the literature.

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Communication strategies to ensure generic networked observability in multi-agent systems

Cited by 21 publications

References 17 publications

Observability of complex systems

Observability of complex systems

A sensor placement and network design paradigm for future smart grids

Single-Bit Consensus With Finite-Time Convergence: Theory and Applications

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