Optimal power management in wireless control systems

Gatsis, Konstantinos; Ribeiro, Alejandro; Pappas, George J.

doi:10.1109/acc.2013.6580058

Cited by 35 publications

(39 citation statements)

References 32 publications

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“…The probability that sensors transmit (equivalently, states exceed threshold) is kept constant by design (cf. (26)). …”

Section: Numerical Simulationsmentioning

confidence: 93%

“…(1) can be generalized in several ways. First, even without interference from simultaneous transmissions, a message may not always be successfully decoded at the controller due to noise added to the transmitted signal or due to wireless fading effects [26]. If successful decoding occurs with some constant probability ∈ [0, 1], the right hand side in (1) needs to be modified to…”

Section: Examplementioning

confidence: 99%

“…On the other hand if the disturbances have distributions different than Gaussian, Theorem 2 still holds. To derive the appropriate thresholds (26), (29) some inversion of these distributions is required, which might be computationally involved. These cases, as well as the case where the distributions are not known, is a subject of future work.…”

Section: Remarkmentioning

confidence: 99%

“…In particular they are used to select the appropriate thresholds on the plant states in (26), (29). On the other hand if the disturbances have distributions different than Gaussian, Theorem 2 still holds.…”

Section: Remarkmentioning

confidence: 99%

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Control-Aware Random Access Communication

Gatsis¹,

Ribeiro²,

Pappas³

2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

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In modern control applications multiple wireless sensors need to efficiently share the available wireless medium to communicate with their respective actuators. Random access policies, where each sensor independently decides whether to access the shared wireless medium, are attractive as they do not require central coordination. However interference between simultaneous transmissions causes transmitted packets to collide, leading to control performance degradation or potentially instability. Given plant and controller dynamics, we derive a sufficient condition for the access policy employed by each sensor so that wireless interference does not violate stability of any involved control loop. Based on this decoupling condition we design random access communication policies that are control-aware by adapting to the physical plant states measured by the sensors online. The control performance of our design is illustrated in numerical simulations.

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“…The probability that sensors transmit (equivalently, states exceed threshold) is kept constant by design (cf. (26)). …”

Section: Numerical Simulationsmentioning

confidence: 93%

Section: Examplementioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

See 2 more Smart Citations

Control-Aware Random Access Communication

Gatsis¹,

Ribeiro²,

Pappas³

2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

Self Cite

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“…Remark 2.1: The above communication model is common for modelling wireless fading channels, which can be found in [8], [10], [11]. Note that this model encompasses, as special case, the i.i.d.…”

Section: A Communication Modelmentioning

confidence: 98%

Bayesian quickest change detection under energy constraints over wireless sensor networks with correlated fading channels

Ren

Shi

2014

11th IEEE International Conference on Control &Amp; Automation (ICCA)

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In the existing approaches to Bayesian quickest change detection under energy constraints, the consumed energy is formulated as the number of observations and the communication channels between the sensors and the fusion center are assumed to be perfect, which are not quite realistic. We consider the correlated wireless fading channels and the associated packet loss probabilities depend on both time-varying channel gains and the power levels being used by the sensors. The channel gains are governed by a first-order stationary and homogeneous Markovian process and the power level can be adjusted by the fusion center. The optimal power control and stopping rules are studied to minimize the average detection delay as well as to satisfy certain energy constraints. This optimization problem is solved by formulating it as a partially observable Markov decision process (POMDP) and the optimal stopping rules are shown to have "weak" threshold structure. A numerical example is given to illustrate the main results. I. INTROCUTIONThe central Bayesian quickest change detection problem was proposed by Shiryaev [1], in which the change time is assumed to have the a priori geometric distribution, a sequence of observations are taken and the goal is to find the optimal stopping rule to minimize the average delay subject to certain false alarm rate. The decentralized counterpart is studied in [2], [3] and some useful results about quantization policy at the side of sensors are obtained.The above literature, however, did not consider energy constraints, which is of ultimate importance in applications using wireless sensor networks (WSNs). This is because the sensors are usually battery-powered and have limited energy. Recently, the problem of quickest change detection with energy constraints are studied in [4]-[7]. The energy consumed in these papers is formulated as the number of observations taken and the sensors are switched off when necessary to conserve energy. The optimal sleep/awake and stopping rules are studied.The limit of the above models, however, is that the authors assumed the communication channels between the sensors and the fusion center are perfect and each observation consumes equal energy, which is not quite realistic in WSNs.The authors are with the This work is supported by an HKUST Caltech Partnership FP004.Packet dropout is common in wireless communications due to many factors, such as fading channels, interference and low signal-to-noise ratio (SNR). In this paper, we consider the scenario where the data sent from the sensor nodes to the fusion center may get dropped over the fading channels. Packet dropout probabilities depend on both power levels of sensors and time-varying channel gains. The channel gains may be correlated with each other (see details in Section II-A). At each time slot, the fusion center makes a global decision based on available observations collected from the sensors and the state of communication channels to stop and declare the change or to continue. If continues, the center needs ...

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Networked control of battery‐powered systems with communication scheduling and power allocation

Wang

Guo

Zhuang

2017

Intl J Robust & Nonlinear

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Summary This paper investigates networked control of a collection of battery‐powered systems with seriously limited communication capacity and power resources. We aim to stabilize the systems by effectively assigning the communication channels and appropriately allocating the transmission powers so that the energy consumption is within an energy budget. The role of channel assignment is to guarantee network access for all plants when needed; the mission of power allocation is to ensure a desired rate of successful packet transmission for each channel. These two aspects are achieved by a scheduling policy and a power allocation method, respectively, each of which is derived based on stability and schedulability requirements. An interesting co‐design framework is derived for communication scheduling, transmission power allocation and stabilizing control. The presented methodology can guarantee a desired decay rate and a given energy consumption for each plant. The effectiveness of the results is demonstrated by numerical simulations. Copyright © 2017 John Wiley & Sons, Ltd.

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Optimal power management in wireless control systems

Cited by 35 publications

References 32 publications

Control-Aware Random Access Communication

Control-Aware Random Access Communication

Bayesian quickest change detection under energy constraints over wireless sensor networks with correlated fading channels

Networked control of battery‐powered systems with communication scheduling and power allocation

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