Algorithms for joint sensor and control nodes selection in dynamic networks

Nugroho, Sebastian A.; Taha, Ahmad F.; Gatsis, Nikolaos; Summers, Tyler H.; Krishnan, Ram

doi:10.1016/j.automatica.2019.04.047

Cited by 17 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, our prior work [15] reformulates the SSP P2 into covex MISDP using McCormick's relaxation [24]. This is not the only method to convert MIBMI into MISDP: the big-M method, which is popular in disjunctive programming, can also be employed-see [6]. The Mc-Cormick's reformulation is performed by defining a new matrix variable M := Y Γ where M ∈ R nx×ny given that Y is bounded such that Y ≤ Y ≤ Ȳ , while the big-M assumes that −L1 ≤ Y ≤ L1 for a sufficiently large constant L > 0.…”

Section: From Mibmi To Convex Misdpmentioning

confidence: 99%

“…Extensive studies have been carried out in the literature to address SASP for particularly linear dynamic systemssee [3][4][5][6] for some notable references. Unlike SASP in linear(ized) systems which yields feasible or optimal sensor or actuator (SA) configuration for specific operating points, SASP for nonlinear dynamic systems (NDS) offers SA selection that are applicable for a much larger operating regions or even globally-this is demonstrated in our prior work through a simple example [7].…”

Section: Introduction and Paper Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Towards Understanding Sensor and Control Nodes Selection in Nonlinear Dynamic Systems: Lyapunov Theory Meets Branch-and-Bound

Nugroho¹,

Taha²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Sensor and actuator selection problems (SASP) are some of the core problems in dynamic systems design and control. These problems correspond to determining the optimal selection of sensors (measurements) or actuators (control nodes) such that certain estimation/control objectives can be achieved. While the literature on SASP is indeed inveterate, the vast majority of the work focuses on linear(ized) representation of the network dynamics, resulting in the placements of sensors or actuators (SA) that are valid for confined operating regions. As an alternative, herein we propose a new general framework for addressing SASP in nonlinear dynamic systems (NDS), assuming that the inputs and outputs are linearly coupled with the nonlinear dynamics. This is investigated through (i) classifying and parameterizing the NDS into various nonlinear function sets, (ii) utilizing rich Lyapunov theoretic formulations, and (iii) designing a new customized branch-and-bound (BnB) algorithm that exploits problem structure of the SASP. The newly designed BnB routines are computationally more attractive than the standard one and also directly applicable to solve SASP for linear systems. In contrast with contemporary approaches from the literature, our approach is suitable for finding the optimal SA combination for stable/unstable NDS that ensures stabilization of estimation error and closed-loop dynamics through a simple linear feedback control policy.

show abstract

Section: From Mibmi To Convex Misdpmentioning

confidence: 99%

Section: Introduction and Paper Contributionsmentioning

confidence: 99%

Towards Understanding Sensor and Control Nodes Selection in Nonlinear Dynamic Systems: Lyapunov Theory Meets Branch-and-Bound

Nugroho¹,

Taha²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To solve P2, one reasonable approach is to transform P2 into MISDP. The reformulation of P2 from nonconvex MISDP to convex MISDP can be carried out by either using big-M method [7], [24] or McCormick's relaxation [6], [25]. With that in mind, here we present a way of reformulating P2 into MISDP via McCormick's relaxation.…”

Section: Convex Misdp Formulations Of Spp For Nds a The Case Formentioning

confidence: 99%

Sensor Placement Strategies for Some Classes of Nonlinear Dynamic Systems via Lyapunov Theory

Nugroho

Taha

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

Self Cite

View full text Add to dashboard Cite

In this paper, the problem of placing sensors for some classes of nonlinear dynamic systems (NDS) is investigated. In conjunction with mixed-integer programming, classical Lyapunov-based arguments are used to find the minimal sensor configuration such that the NDS internal states can be observed while still optimizing some estimation metrics. The paper's approach is based on two phases. The first phase assumes that the encompassed nonlinearities belong to one of the following function set classifications: bounded Jacobian, Lipschitz continuous, one-sided Lipschitz, or quadratically inner-bounded. To parameterize these classifications, two approaches based on stochastic point-based and interval-based optimization methods are explored. Given the parameterization, the second phase formulates the sensor placement problem for various NDS classes through mixed-integer convex programming. The theoretical optimality of the sensor placement alongside a state estimator design are then given. Numerical tests on traffic network models showcase that the proposed approach yields sensor placements that are consistent with conventional wisdom in traffic theory.

show abstract

“…The proof is omitted from this version of the work, and will be included in the extended version of the manuscript [26].…”

Section: Disjunctive Programming For Saa Selectionmentioning

confidence: 99%

Simultaneous Sensor and Actuator Selection/Placement through Output Feedback Control

Nugroho

Taha

Summers

et al. 2018

2018 Annual American Control Conference (ACC)

Self Cite

View full text Add to dashboard Cite

In most dynamic networks, it is impractical to measure all of the system states; instead, only a subset of the states are measured through sensors. Consequently, and unlike full state feedback controllers, output feedback control utilizes only the measured states to obtain a stable closed-loop performance. This paper explores the interplay between the selection of minimal number of sensors and actuators (SaA) that yield a stable closed-loop system performance. Through the formulation of the static output feedback control problem, we show that the simultaneous selection of minimal set of SaA is a combinatorial optimization problem with mixedinteger nonlinear matrix inequality constraints. To address the computational complexity, we develop two approaches: The first approach relies on integer/disjunctive programming principles, while the second approach is a simple algorithm that is akin to binary search routines. The optimality of the two approaches is also discussed. Numerical experiments are included showing the performance of the developed approaches.Index Terms-Sensor and actuator selection and placement, static output feedback control, mixed-integer nonlinear matrix inequality, disjunctive programming, binary search algorithm.

show abstract

Algorithms for joint sensor and control nodes selection in dynamic networks

Cited by 17 publications

References 24 publications

Towards Understanding Sensor and Control Nodes Selection in Nonlinear Dynamic Systems: Lyapunov Theory Meets Branch-and-Bound

Towards Understanding Sensor and Control Nodes Selection in Nonlinear Dynamic Systems: Lyapunov Theory Meets Branch-and-Bound

Sensor Placement Strategies for Some Classes of Nonlinear Dynamic Systems via Lyapunov Theory

Simultaneous Sensor and Actuator Selection/Placement through Output Feedback Control

Contact Info

Product

Resources

About