Near Optimal Rate Selection for Wireless Control Systems

Saifullah, Abusayeed; Wu, Chengjie; Tiwari, Paras Babu; Xu, Yingcheng; Fu, Yong; Lu, Chenyang; Chen, Yixin

doi:10.1109/rtas.2012.21

Cited by 28 publications

(9 citation statements)

References 23 publications

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“…Remind that Kalman filtering is one of the most popular approaches to estimate the plant state and works well if there is no significant message loss [22]. Since the control command directly affects the plant, controller-actuator dropouts are more critical than sensor-controller dropouts [101], [102]. Many practical NCSs have several sensor-controller channels whereas the controllers are collocated with the actuators, e.g., heat, ventilation and air-conditioning control systems [103].…”

Section: Message Dropout 1) Control System Aspectmentioning

confidence: 99%

“…Control Parameters [125] 1-hop the/sim [173] 1-hop the/exp [129] 1-hop the/exp [174] 1-hop the/sim [175] multihop the/sim [126] 1-hop the/sim/exp [127] 1-hop the/sim/exp [176] 1-hop the [177] 1-hop sim [178] 1-hop exp [86] multihop the/sim [179] multihop the/sim [180], [181] multihop the/sim [182] multihop the/sim/exp [183] multihop the/exp [184] 1-hop the/sim [185] multihop the/exp [186] 1-hop the/sim/exp [87] 1-hop the/sim [187] multihop the/sim/exp [188] multihop the/sim [141], [142] multihop the/sim [138] multihop the/sim [6] 1-hop the/sim [102] multihop the/sim [18] multihop the/exp [189] multihop the/sim/exp [4] 1-hop the/sim [190] 1-hop the/sim [66] multihop the/sim [191] multihop the/sim [192], [193] 1-hop the/sim/exp [194], [195], [196] 1-hop the/sim [63] multihop sim/exp [197] multihop sim [107] 1-hop the/sim/exp [198] multihop the/sim [199] multihop the/sim/exp…”

Section: Requirements System Parameters Scenarios Evaluation Communicmentioning

confidence: 99%

See 1 more Smart Citation

Wireless Network Design for Control Systems: A Survey

Park

Coleri

Fischione

et al. 2018

IEEE Commun. Surv. Tutorials

Self Cite

378

280

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Abstract-Wireless networked control systems (WNCS) are composed of spatially distributed sensors, actuators, and controllers communicating through wireless networks instead of conventional point-to-point wired connections. Due to their main benefits in the reduction of deployment and maintenance costs, large flexibility and possible enhancement of safety, WNCS are becoming a fundamental infrastructure technology for critical control systems in automotive electrical systems, avionics control systems, building management systems, and industrial automation systems. The main challenge in WNCS is to jointly design the communication and control systems considering their tight interaction to improve the control performance and the network lifetime. In this survey, we make an exhaustive review of the literature on wireless network design and optimization for WNCS. First, we discuss what we call the critical interactive variables including sampling period, message delay, message dropout, and network energy consumption. The mutual effects of these communication and control variables motivate their joint tuning. We discuss the effect of controllable wireless network parameters at all layers of the communication protocols on the probability distribution of these interactive variables. We also review the current wireless network standardization for WNCS and their corresponding methodology for adapting the network parameters. Moreover, we discuss the analysis and design of control systems taking into account the effect of the interactive variables on the control system performance. Finally, we present the state-of-the-art wireless network design and optimization for WNCS, while highlighting the tradeoff between the achievable performance and complexity of various approaches. We conclude the survey by highlighting major research issues and identifying future research directions.

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Section: Message Dropout 1) Control System Aspectmentioning

confidence: 99%

Section: Requirements System Parameters Scenarios Evaluation Communicmentioning

confidence: 99%

Wireless Network Design for Control Systems: A Survey

Park

Coleri

Fischione

et al. 2018

IEEE Commun. Surv. Tutorials

Self Cite

378

280

View full text Add to dashboard Cite

show abstract

“…One of our main contributions is then to show that the optimal allocation problem can be solved exactly for τ asymptotically small (or equivalently, the sampling rate asymptotically high). We note here that the question about the dependence of the estimation error on the sampling rates, and the optimal allocation problem, have also been addressed recently in the computer science and cyber-physical systems communities [27]- [29]. This line of work, however, relies on a heuristic claim that the performance measure (here, the total estimation error) decays exponentially in the sampling rate.…”

Section: Introductionmentioning

confidence: 99%

Optimal capacity allocation for sampled networked systems

2017

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We consider the problem of estimating the states of weakly coupled linear systems from sampled measurements.We assume that the total capacity available to the sensors to transmit their samples to a network manager in charge of the estimation is bounded above, and that each sample requires the same amount of communication. Our goal is then to find an optimal allocation of the capacity to the sensors so that the average estimation error is minimized.We show that when the total available channel capacity is large, this resource allocation problem can be recast as a strictly convex optimization problem, and hence there exists a unique optimal allocation of the capacity. We further investigate how this optimal allocation varies as the available capacity increases. In particular, we show that if the coupling among the subsystems is weak, then the sampling rate allocated to each sensor is nondecreasing in the total sampling rate, and is strictly increasing if and only if the total sampling rate exceeds a certain threshold.the same for all pair (i, j), for i = j, are made to simplify the notations of the paper, but are not necessary for the results to hold. The Brownian motions w i are pairwise independent and the ν i (kτ 0 ) are pairwise independent normal random variables. The w i and ν i are also assumed to be independent.We refer to the system described in (1) as subsystem S i . The samples y i (kτ 0 ), k ∈ N, are sent over a common channel to a network manager whose objective is to estimate the states x i of the subsystems S i , for all i = 1, . . . , N , from these samples. The network manager needs to decide the schedule with which it receives the samples in order to minimize the estimation error. Note that since the systems are coupled, the knowledge of y i can help with the estimation of x j , for i = j.

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“…Wireless control has been widely studied in different areas (i.e., industry process automation [13,14] and unmanned vehicle [15]). Tools for simulating the wireless network and control system exist such as Truetime [16] and PiccSIM [17].…”

Section: Introductionmentioning

confidence: 99%

Benchmark problem in active structural control with wireless sensor network

Sun

Dyke

et al. 2015

Struct. Control Health Monit.

Self Cite

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Summary Structural control systems offer an attractive approach to protect civil infrastructures from natural hazards such as earthquakes. Wireless structural control systems that utilize wireless sensors for sensing, communication, and control have drawn increased attention because of the flexible installation, rapid deployment, and reduced cost. Although there are studies of wireless control systems for civil structures, a benchmark problem that captures not only the dynamics of the plant but also the realistic features of a wireless network has not been available. In this paper, a benchmark model for an active mass driver system with a wireless sensor network is presented. This wireless control benchmark model combines a seismically excited building benchmark model developed with Simulink (Matlab, MathWorks, Inc., Natick, MA, USA) and a wireless sensor network implemented in simulation using a state‐of‐the‐art wireless simulator TOSSIM (UC Berkeley, Berkeley, CA, USA). Wireless signal and noise traces collected from a real‐world multistory building are used as inputs to TOSSIM to realistically simulate the wireless sensor network. Wireless control design issues such as network‐induced delay, data loss, available sensor measurements, measurement noises, and control constraints can be studied with this benchmark model. A sample controller is provided to illustrate the wireless control design. Evaluation criteria have been provided to examine resources and control performances. Copyright © 2015 John Wiley & Sons, Ltd.

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Near Optimal Rate Selection for Wireless Control Systems

Cited by 28 publications

References 23 publications

Wireless Network Design for Control Systems: A Survey

Wireless Network Design for Control Systems: A Survey

Optimal capacity allocation for sampled networked systems

Benchmark problem in active structural control with wireless sensor network

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