Adaptive controller placement for wireless sensor–actuator networks with erasure channels

Quevedo, Daniel E.; Johansson, Karl Henrik; Åhlén, Anders; Jurado, Isabel

doi:10.1016/j.automatica.2013.08.024

Cited by 17 publications

(18 citation statements)

References 20 publications

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“…This proof follows the procedure of [23]. Using the law of total expectation, we have E Θ(k + 1)Θ T (k + 1) = j∈B Q j (k + 1), (B.9) with Q j (k + 1) = E Θ(k + 1)Θ T (k + 1) Ξ(k) = j…”

Section: Appendix B Proof Of Lemmamentioning

confidence: 97%

Shaped Gaussian Dictionaries for Quantized Networked Control Systems With Correlated Dropouts

Peters

Quevedo

Østergaard

2016

IEEE Trans. Signal Process.

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This paper studies fixed rate vector quantisation for noisy networked control systems (NCSs) with correlated packet dropouts. In particular, a discrete-time linear time invariant system is to be controlled over an error-prone digital channel. The controller uses (quantized) packetized predictive control to reduce the impact of packet losses. The proposed vector quantizer is based on sparse regression codes (SPARC), which have recently been shown to be efficient in open-loop systems when coding white Gaussian sources. The dictionaries in existing design of SPARCs consist of independent and identically distributed (i.i.d.) Gaussian entries. However, we show that a significant gain can be achieved by using Gaussian dictionaries that are shaped according to the second-order statistics of the NCS in question. Furthermore, to avoid training of the dictionaries, we provide closed-form expressions for the required second-order statistics in the absence of quantization.

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Section: Appendix B Proof Of Lemmamentioning

confidence: 97%

Shaped Gaussian Dictionaries for Quantized Networked Control Systems With Correlated Dropouts

Peters

Quevedo

Østergaard

2016

IEEE Trans. Signal Process.

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“…which has the same form as (18). Thus, repeat the same process as that for L = N − 1, we can derive the optimal state-feedback control law u i,k , k = N − 2, · · · , 1, 0, which is given by…”

Section: ) L = Nmentioning

confidence: 99%

“…Ref. [18] exploits the flexibility of control design in WSANs to study the adaptive controller placement. The optimal LQ Gaussian control problem is considered in [19] [20] with the signal estimation over lossy networks.…”

Section: Introductionmentioning

confidence: 99%

Stochastic optimal linear control of wireless networked control systems with delays and packet losses

Wang

Wang²,

Liu

2016

IET Control Theory & Applications

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In this paper, the design of the optimal decentralized state-feedback controllers is considered for a wireless sensor and actuator network (WSAN) with stochastic network-induced delays and packet losses. In particular, taking advantage of multiple controllers, we model the WSAN as a wireless networked control system (NCS) with decentralized controllers, and then formulate the stochastic optimal state-feedback control problem as a non-cooperative linear quadratic (LQ) game. The optimal control law of each controller is obtained that is a function of the current plant state and all past control signals. The performance of the proposed stochastic optimal control algorithm is investigated using both a genetic control system and a load frequency control (LFC) system in power grid. Index TermsWireless sensor and actuator network (WSAN), networked control system (NCS), decentralized controllers, delays, packet losses, non-cooperative game.

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“…Traditionally, the packet dropouts are modeled as an independent and identically distributed ( i.i.d ) random process . This model is also extended to include transmission delays . For many network setups, the network effects can however not be captured by an i.i.d model, since the packet dropouts in the networks often are correlated .…”

Section: Introductionmentioning

confidence: 99%

Predictive control for networked systems affected by correlated packet loss

Peters

Marelli

Quevedo

et al. 2017

Intl J Robust & Nonlinear

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In this paper, we consider the predictive control problem of designing receding horizon controllers for networked linear systems subject to random packet loss in the controller to actuator link. The packet dropouts are temporarily correlated in the sense that they obey a Markovian transition model. Our design task is to solve the optimal controller that minimizes a given receding horizon cost function, using the available packet loss history. Due to the correlated nature of the packet loss, standard linear quadratic regulator methods do not apply. We first present the optimal control law by considering the correlations. This controller turns out to depend on the packet loss history and would typically require a large lookup table for implementation when the Markovian order is high. To address this issue, we present and compare several suboptimal design approaches to reduce the number of control laws. KEYWORDSjump linear systems, networked control, optimal control, packet dropouts, predictive control, stochastic systems 5078

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Adaptive controller placement for wireless sensor–actuator networks with erasure channels

Cited by 17 publications

References 20 publications

Shaped Gaussian Dictionaries for Quantized Networked Control Systems With Correlated Dropouts

Shaped Gaussian Dictionaries for Quantized Networked Control Systems With Correlated Dropouts

Stochastic optimal linear control of wireless networked control systems with delays and packet losses

Predictive control for networked systems affected by correlated packet loss

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