Estimation under uncontrolled and controlled communications in Networked Control Systems

Xu, Yonggang; Hespanha, João P.

doi:10.1109/cdc.2005.1582262

Cited by 58 publications

(4 citation statements)

References 14 publications

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“…Generally, sending the estimate enables the remote estimator encode more information compared to sending the measurement [28]. The event trigger decides whether to send out the Kalman estimate xs k through the communication network.…”

Section: A System Modelmentioning

confidence: 99%

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Optimal Finite Horizon Scheduling of Wireless Networked Control Systems

Ayan

Hirche

Ephremides

et al. 2024

IEEE/ACM Trans. Networking

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Emerging cyber-physical systems impel the development of communication protocols to efficiently utilize resources. This article investigates infinite-horizon optimal scheduling for resource-aware networked control systems by addressing the rate-regulation tradeoff. Consider the scenario in which the sensor and the controller communicate via a networked channel, the transmission scheduling problem is formulated as a Markov decision process on unbounded continuous state space controlled by scheduling decisions. Value of information (VoI) is employed as a metric for evaluating the importance of datas being transmitted. The optimal scheduling law based on VoI is shown to be deterministic and stationary, of which expression is obtained using value iteration. The closed-loop system under the designed scheduling law is stochastically stable. By analyzing the dynamic behavior of the iteration procedure, we show that the VoI function and the optimal scheduling law are symmetric. Moreover, when the system matrix is diagonalizable, the VoI function is monotone and quasi-convex. Based on this, the optimal scheduling law is shown to be of threshold type and quadratic form. The region of the threshold value is provided. Finally, the numerical simulation illustrates the theoretical result of the VoI-based scheduling.

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Section: A System Modelmentioning

confidence: 99%

“…We expand the right-hand side of Bellman equation ( 20) as follows: (28) where the random variable ξ follows the distribution N (0, Ξ), and E[h(Ae + ξ)] = y∈X h(y)p ξ (y − Ae)dy, E[h(ξ)] = y∈X h(y)p ξ (y)dy denote the expected differential costs.…”

Section: Optimal Stationary Schedulingmentioning

confidence: 99%

Optimal Finite Horizon Scheduling of Wireless Networked Control Systems

Ayan

Hirche

Ephremides

et al. 2024

IEEE/ACM Trans. Networking

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“…Similar to [14], we employ a pre-estimator before transmission to avoid remote estimation based on the outputs. We assume the input signal is known to the pre-estimator before transmission, but the remote state estimator has no access to either the inputs or the control laws.…”

Section: Introductionmentioning

confidence: 99%

Reachable set estimation for discrete-time T-S fuzzy singular systems based on Piecewise Lyapunov function

Shi

Feng

2018

2018 Chinese Control and Decision Conference (CCDC)

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We employ reachability analysis in designing dynamic quantization schemes for the remote state estimation of linear systems over a finite date rate communication channel. The quantization region is dynamically updated at each transmission instant, with an approximated reachable set of the linear system. We propose a set-based method using zonotopes and compare it to a norm-based method in dynamically updating the quantization region. For both methods, we guarantee that the quantization error is bounded and consequently, the remote state reconstruction error is also bounded. To the best of our knowledge, the set-based method using zonotopes has no precedent in the literature and admits a larger class of linear systems and communication channels, where the set-based method allows for a longer inter-transmission time and lower bit rate. Finally, we corroborate our theoretical guarantees with a numerical example.

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“…Potentially, there is insufficient time to transfer the data. 12,13,14 Additionally, serial communication has no inherent mechanism to synchronize the transmitted information, which previously occurred by simultaneous sampling of continuous analog signals in a centralized architecture. This leads directly to the need to understand and model the effect of the sequential flow of data in a distributed control simulation.…”

mentioning

confidence: 99%

A Network Scheduling Model for Distributed Control Simulation

Culley

Thomas²,

Aretskin-Hariton³

2016

52nd AIAA/SAE/ASEE Joint Propulsion Conference

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Distributed engine control is a hardware technology that radically alters the architecture for aircraft engine control systems. Of its own accord, it does not change the function of control, rather it seeks to address the implementation issues for weight-constrained vehicles that can limit overall system performance and increase life-cycle cost. However, an inherent feature of this technology, digital communication networks, alters the flow of information between critical elements of the closed-loop control. Whereas control information has been available continuously in conventional centralized control architectures through virtue of analog signaling, moving forward, it will be transmitted digitally in serial fashion over the network(s) in distributed control architectures. An underlying effect is that all of the control information arrives asynchronously and may not be available every loop interval of the controller, therefore it must be scheduled. This paper proposes a methodology for modeling the nominal data flow over these networks and examines the resulting impact for an aero turbine engine system simulation.2 the control law and repositioned so that it no longer needs to be encumbered by a harsh thermal environment due to the previous constraints.The control architecture described above is only realizable if information is sufficiently free to flow between the distributed hardware elements. Using digital network technology potentially resolves this issue by using a low weight wire harness with a common interface at each hardware element. The trade-off, however, is a sequential flow of digital data between these elements, leading to an inherent skew in the arrival time of information to and from the controller. Potentially, there is insufficient time to transfer the data. 12,13,14 Additionally, serial communication has no inherent mechanism to synchronize the transmitted information, which previously occurred by simultaneous sampling of continuous analog signals in a centralized architecture. This leads directly to the need to understand and model the effect of the sequential flow of data in a distributed control simulation. The purpose of a communication schedule model is to simulate the nominal exchange of information, i.e., non-fault condition, that occurs between the hardware controller and control elements. This is inherently a different problem than trying to analyze fault conditions like stability under packet loss, 15 which is not considered here.In the near term, relative to a traditional centralized control architecture, DEC technology can provide aerodynamic and thermodynamic engine system benefits, improve engine life cycle costs, and reduce the total engine system weight. These near term objectives tend to focus on overcoming existing system constraints. For example, i) DEC hardware can be more easily configured to reduce engine drag by limiting the nacelle diameter, ii) high temperature electronics can improve heat quality, rejecting heat at higher temperature for more efficient cooling, while...

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Estimation under uncontrolled and controlled communications in Networked Control Systems

Cited by 58 publications

References 14 publications

Optimal Finite Horizon Scheduling of Wireless Networked Control Systems

Optimal Finite Horizon Scheduling of Wireless Networked Control Systems

Reachable set estimation for discrete-time T-S fuzzy singular systems based on Piecewise Lyapunov function

A Network Scheduling Model for Distributed Control Simulation

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