Data Transmission Over Networks for Estimation and Control

Gupta, Vijay; Amir, Dana; Hespanha, João P.; Murray, Richard M.; Hassibi, Babak

doi:10.1109/tac.2009.2024567

Cited by 180 publications

(192 citation statements)

References 23 publications

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“…However, the presence of a network within the control loop can adversely affect the performance of the system due to the inherent unreliability (e.g., packet drops or transmission latency) of the communication channel. Such anomalies have direct consequences on the stability of the plant, as shown in previous studies [1,2,7,8,11,17,19].…”

Section: Introductionmentioning

confidence: 54%

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HMM-based characterization of channel behavior for networked control systems

Chang

Venkatasubramanian

Enyioha

et al. 2012

Proceedings of the 1st International Conference on High Confidence Networked Systems

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We study the problem of characterizing the behavior of lossy and data corrupting communication channels in a networked control setting, where the channel's behavior exhibits temporal correlation. We propose a behavior characterization mechanism based on a hidden Markov model (HMM). The use of a HMM in this regard presents multiple challenges including dealing with incomplete observation sequences (due to data losses and corruptions) and the lack of a priori information about the model complexity (number of states in the model). We address the first challenges by using the plant state information and history of received/ applied control inputs to fill in the gaps in the observation sequences, and by enhancing the HMM learning algorithm to deal with missing observations . Further, we adopt two model quality criteria for determining behavior model complexity. The contributions of this paper include: (1) an enhanced learning algorithm for refining the HMM model parameters to handle missing observations, and (2) simultaneous use of two welldefined model quality criteria to determine the model complexity. Simulation results demonstrate over 90\% accuracy in predicting the output of a channel at a given time step, when compared to a traditional HMM based model that requires complete knowledge of the model complexity and observation sequence. ABSTRACTWe study the problem of characterizing the behavior of lossy and data corrupting communication channels in a networked control setting, where the channel's behavior exhibits temporal correlation. We propose a behavior characterization mechanism based on a hidden Markov model (HMM). The use of a HMM in this regard presents multiple challenges including dealing with incomplete observation sequences (due to data losses and corruptions) and the lack of a priori information about the model complexity (number of states in the model). We address the first challenges by using the plant state information and history of received/applied control inputs to fill in the gaps in the observation sequences, and by enhancing the HMM learning algorithm to deal with missing observations . Further, we adopt two model quality criteria for determining behavior model complexity. The contributions of this paper include: (1) an enhanced learning algorithm for refining the HMM model parameters to handle missing observations, and (2) simultaneous use of two well-defined model quality criteria to determine the model complexity. Simulation results demonstrate over 90% accuracy in predicting the output of a channel at a given time step, when compared to a traditional HMM based model that requires complete knowledge of the model complexity and observation sequence.

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Section: Introductionmentioning

confidence: 54%

“…We assume that the system is designed in such a way that stability (in an appropriate sense) is attained despite the presence of packet drops (e.g., if the conditions provided in works such as [7,9,15,24], etc. are satisfied).…”

Section:   mentioning

confidence: 99%

HMM-based characterization of channel behavior for networked control systems

Chang

Venkatasubramanian

Enyioha

et al. 2012

Proceedings of the 1st International Conference on High Confidence Networked Systems

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“…Since the above equation has the standard observer structure [23], we refer to this scheme as the WCN with observer style updates (as in [24]). …”

Section: Wcn With Observer Style Updatesmentioning

confidence: 99%

Closing the loop: A simple distributed method for control over wireless networks

Pajić

Sundaram

et al. 2012

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)

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We present a distributed scheme used for control over a network of wireless nodes. As opposed to traditional networked control schemes where the nodes simply route information to and from a dedicated controller (perhaps performing some encoding along the way), our approach, Wireless Control Network (WCN), treats the network itself as the controller. In other words, the computation of the control law is done in a fully distributed way inside the network. We extend the basic WCN strategy, where at each time-step, each node updates its internal state to be a linear combination of the states of the nodes in its neighborhood. This causes the entire network to behave as a linear dynamical system, with sparsity constraints imposed by the network topology. We demonstrate that with observer style updates, the WCN's robustness to link failures is substantially improved. Furthermore, we show how to design a WCN that can maintain stability even in cases of node failures. We also address the problem of WCN synthesis with guaranteed optimal performance of the plant, with respect to standard cost functions. We extend the synthesis procedure to deal with continuoustime plants and demonstrate how the WCN can be used on a practical, industrial application, using a processin-the-loop setup with real hardware. KeywordsCooperative control, decentralized control, networked control systems, wireless sensor networks ABSTRACTWe present a distributed scheme used for control over a network of wireless nodes. As opposed to traditional networked control schemes where the nodes simply route information to and from a dedicated controller (perhaps performing some encoding along the way), our approach, Wireless Control Network (WCN), treats the network itself as the controller. In other words, the computation of the control law is done in a fully distributed way inside the network. We extend the basic WCN strategy, where at each time-step, each node updates its internal state to be a linear combination of the states of the nodes in its neighborhood. This causes the entire network to behave as a linear dynamical system, with sparsity constraints imposed by the network topology. We demonstrate that with observer style updates, the WCN's robustness to link failures is substantially improved. Furthermore, we show how to design a WCN that can maintain stability even in cases of node failures. We also address the problem of WCN synthesis with guaranteed optimal performance of the plant, with respect to standard cost functions. We extend the synthesis procedure to deal with continuoustime plants and demonstrate how the WCN can be used on a practical, industrial application, using a process-in-the-loop setup with real hardware.

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“…On the other hand, the study of time-delay and packet-dropouts mainly focuses on periodic transmission schemes [8,19] or on finding upper bounds on the sampling period [10]. In [18] the design of optimal level-triggered impulse control under packet dropouts with multiple loops sharing a common network is considered.…”

Section: Introductionmentioning

confidence: 99%

Suboptimal event‐triggered control for networked control systems

Molin

Hirche

2013

Z Angew Math Mech

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Key words Networked control systems, control over packet-switched communications, event-triggered control, stochastic optimal control, long-run average cost optimization, stochastic stability, drift criteria.The advent of networked control systems urges the digital control design to incorporate communication constraints efficiently. In order to accommodate this requirement, this article studies the joint design of controller and event-trigger for linear stochastic systems in the presence of a resource-limited communication channel which exhibits packet dropouts and time-delay. The event-trigger situated at the sensor decides at every sampling instance, whether to send information over the communication channel to the controller. The design approach is formulated as a stochastic average-cost optimization problem, where the communication constraints are reflected as an additional cost penalty of the average transmission rate. Different conditions on the communication model are given where the joint optimal design can be split into a separate control and event-trigger design. Based on these results, two suboptimal design approaches are developed. By using drift criteria, stability guarantees of the closed-loop system for both approaches are derived in terms of bounded moment stability. Numerical simulations illustrate the efficacy of the event-triggered approach compared with optimal time-triggered controllers.

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Data Transmission Over Networks for Estimation and Control

Cited by 180 publications

References 23 publications

HMM-based characterization of channel behavior for networked control systems

HMM-based characterization of channel behavior for networked control systems

Closing the loop: A simple distributed method for control over wireless networks

Suboptimal event‐triggered control for networked control systems

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