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Wireless routing and control: a cyber-physical case study

Published: 11 April 2016 Publication History

Abstract

Wireless sensor-actuator networks (WSANs) are being adopted in process industries because of their advantages in lowering deployment and maintenance costs. While there has been significant theoretical advancement in networked control design, only limited empirical results that combine control design with realistic WSAN standards exist. This paper presents a cyber-physical case study on a wireless process control system that integrates state-of-the-art network control design and a WSAN based on the WirelessHART standard. The case study systematically explores the interactions between wireless routing and control design in the process control plant. The network supports alternative routing strategies, including single-path source routing and multi-path graph routing. To mitigate the effect of data loss in the WSAN, the control design integrates an observer based on an Extended Kalman Filter with a model predictive controller and an actuator buffer of recent control inputs. We observe that sensing and actuation can have different levels of resilience to packet loss under this network control design. We then propose a flexible routing approach where the routing strategy for sensing and actuation can be configured separately. Finally, we show that an asymmetric routing configuration with different routing strategies for sensing and actuation can effectively improve control performance under significant packet loss. Our results highlight the importance of co-joining the design of wireless network protocols and control in wireless control systems.

References

[1]
http://wsn.cse.wustl.edu/index.php/Testbed.
[2]
Gurobi optimizer. http://www.gurobi.com/index.
[3]
ISA100: Wireless Systems for Automation. http://www.isa100wci.org.
[4]
Wireless Industrial Networking Alliance. http://www.wina.org.
[5]
ZigBee alliance. http://www.zigbee.org.
[6]
WirelessHART specification, 2007. http://www.hartcomm2.org.
[7]
J. Araujo, A. Anta, M. Mazo, J. Faria, A. Hernandez, P. Tabuada, and K. Johansson. Self-triggered control over wireless sensor and actuator networks. In International Conference on Distributed Computing in Sensor Systems and Workshops, pages 1--9, 2011.
[8]
K. J. Aström and R. M. Murray. Feedback systems: an introduction for scientists and engineers. Princeton university press, 2010.
[9]
G. Baliga, S. Graham, L. Sha, and P. Kumar. Etherware: Domainware for wireless control networks. In IEEE International Symposium on Object-oriented Real-time Distributed Computing, 2004.
[10]
A. Bemporad. Predictive control of teleoperated constrained systems with unbounded communication delays. In IEEE Conference on Decision and Control, volume 2, pages 2133--2138. IEEE, 1998.
[11]
A. Bemporad, A. Casavola, and E. Mosca. Nonlinear control of constrained linear systems via predictive reference management. IEEE Transactions on Automatic Control, 42(3):340--349, 1997.
[12]
B. Demirel, Z. Zou, P. Soldati, and M. Johansson. Modular co-design of controllers and transmission schedules in WirelessHART. In IEEE Conference on Decision and Control and European Control Conference, pages 5951--5958, 2011.
[13]
C. E. Garcia, D. M. Prett, and M. Morari. Model predictive control: theory and practice---a survey. Automatica, 25(3):335--348, 1989.
[14]
S. Gobriel, D. Mosse, and R. Cleric. Tdma-asap: Sensor network tdma scheduling with adaptive slot-stealing and parallelism. In IEEE International Conference on Distributed Computing Systems, pages 458--465. IEEE, 2009.
[15]
S. Han, X. Zhu, A. Mok, D. Chen, and M. Nixon. Reliable and real-time communication in industrial wireless mesh networks. In IEEE Real-Time and Embedded Technology and Applications Symposium, 2011.
[16]
J. Heo, J. Hong, and Y. Cho. Earq: Energy aware routing for real-time and reliable communication in wireless industrial sensor networks. IEEE Transactions on Industrial Informatics, 5(1):3--11, 2009.
[17]
K.-D. Kim and P. Kumar. The importance, design and implementation of a middleware for networked control systems. In Networked Control Systems, pages 1--29. Springer, 2010.
[18]
X. Koutsoukos, N. Kottenstette, J. Hall, P. Antsaklis, and J. Sztipanovits. Passivity-based control design for cyber-physical systems. In International Workshop on Cyber-Physical Systems-Challenges and Applications, 2008.
[19]
B. Li, L. Nie, C. Wu, H. Gonzalez, and C. Lu. Incorporating emergency alarms in reliable wireless process control. In ACM/IEEE International Conference on Cyber-Physical Systems, 2015.
[20]
B. Li, Z. Sun, K. Mechitov, C. Lu, D. Dyke, G. Agha, and B. Spencer. Realistic case studies of wireless structural control. In ACM/IEEE International Conference on Cyber-Physical Systems, April 2013.
[21]
B. Li, D. Wang, F. Wang, and Y. Ni. High quality sensor placement for SHM systems: Refocusing on application demands. In IEEE International Conference on Computer Communications, 2010.
[22]
X. Liu and A. Goldsmith. Kalman filtering with partial observation losses. In IEEE Conference on Decision and Control, volume 4, pages 4180--4186. IEEE, 2004.
[23]
C. Lu, A. Saifullah, B. Li, M. Sha, H. Gonzalez, D. Gunatilaka, C. Wu, L. Nie, and Y. Chen. Real-time wireless sensor-actuator networks for industrial cyber-physical systems. Proceedings of the IEEE, 2016.
[24]
M. Pajic, S. Sundaram, J. Le Ny, G. J. Pappas, and R. Mangharam. Closing the loop: a simple distributed method for control over wireless networks. In International Conference on Information Processing in Sensor Networks, IPSN '12, pages 25--36, New York, NY, USA, 2012. ACM.
[25]
K. Pister, P. Thubert, S. Dwars, and T. Phinney. Industrial routing requirements in low-power and lossy networks. Technical report, 2009.
[26]
A. Saifullah, D. Gunatilaka, P. Tiwari, M. Sha, C. Lu, B. Li, C. Wu, and Y. Chen. Schedulability analysis under graph routing in WirelessHART networks. In IEEE Real-Time Systems Symposium, 2015.
[27]
A. Saifullah, C. Wu, P. Tiwari, Y. Xu, Y. Fu, C. Lu, and Y. Chen. Near optimal rate selection for wireless control systems. In IEEE Real-Time and Embedded Technology and Applications Symposium, 2012.
[28]
Y. Shi and H. Fang. Kalman filter-based identification for systems with randomly missing measurements in a network environment. International Journal of Control, 83(3):538--551, 2010.
[29]
B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, and S. Sastry. Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control, 49(9):1453--1464, 2004.
[30]
B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, and S. S. Sastry. Time varying optimal control with packet losses. In IEEE Conference on Decision and Control, volume 2, pages 1938--1943. IEEE, 2004.
[31]
Z. Sun, B. Li, S. Dyke, and C. Lu. Evaluation of performances of structural control benchmark problem with time delays from wireless sensor network. In EMI/PMC'12, 2012.
[32]
Z. Sun, B. Li, S. J. Dyke, C. Lu, and L. Linderman. Benchmark problem in active structural control with wireless sensor network. Structural Control and Health Monitoring, 23(1):20--34, 2016.
[33]
P. Tabuada. Event-triggered real-time scheduling of stabilizing control tasks. IEEE Transactions on Automatic Control, 52(9):1680--1685, 2007.
[34]
C. Wu, D. Gunatilaka, A. Saifullah, M. Sha, P. B. Tiwari, C. Lu, and Y. Chen. Maximizing Network Lifetime of WirelessHART Networks under Graph Routing. In IEEE International Conference on Internet of Things Design and Implementation, April 2016.
[35]
C. Wu, D. Gunatilaka, M. Sha, C. Lu, and Y. Chen. Conflict-aware real-time routing for industrial wireless sensor-actuator networks. In Technical Report WUCSE-2015-005. All Computer Science and Engineering Research, 2015. http://openscholarship.wustl.edu/cse_research/507/.
[36]
Z. Zinonos, R. Silva, V. Vassiliou, and J. S. Silva. Mobility solutions for wireless sensor and actuator networks with performance guarantees. In International Conference on Telecommunications, pages 406--411. IEEE, 2011.

Cited By

View all
  • (2019)Age-of-information vs. value-of-information scheduling for cellular networked control systemsProceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems10.1145/3302509.3311050(109-117)Online publication date: 16-Apr-2019
  • (2019)Feedback control goes wirelessProceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems10.1145/3302509.3311046(97-108)Online publication date: 16-Apr-2019
  • (2019)Sampling rate optimization for IEEE 802.11 wireless control systemsProceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems10.1145/3302509.3311045(87-96)Online publication date: 16-Apr-2019
  • Show More Cited By

Recommendations

Reviews

Mohammad Sadegh Kayhani Pirdehi

Utilization of wireless sensor networks at automated industrial processing plants, not only for monitoring aspects but also for actuator ones, opens a new horizon for their deployment. In this platform, sensing components, control adapters, and actuating entities are the main elements that interact to perform automated industrial processing in an online and sensitive manner. They are connected by a wireless mesh networking platform, a wireless control processing system. In this configuration, information about the environment is transferred by the sensors, appropriate decisions are made by the control section, and the commands are dispatched to the actuators. Packet loss, which is a common issue in wireless networking, is more critical in the link between the control unit and actuators than between the sensors and the control unit. In the former, the command messages should be transferred at a more timely and reliable manner to perform the appropriate action with a defined accuracy and precision. A joint network routing and control mechanism policy is proposed to mitigate the destructive effects of packet loss. In the control system, based on the prediction and update mechanisms at the extended Kalman filter (EKF), with the presence of the uncertainties in the communication media, a state observer is used to determine the next most appropriate state, utilizing a model predictive control (MPC) policy. In addition, a buffer in the actuators is placed to store the received control commands and alleviate the possible packet loss effects. A WirelessHART architecture with physical layer specification IEEE802.15.4 is considered for the network design. The proposed asymmetric routing mechanism is the most significant part of the paper: in response to the different nature of requirements in the network, both source-routing and graph-routing mechanisms are utilized simultaneously for sensor-control and control-actuator links, respectively. A comprehensive case study in accord with its theoretical discussion is presented, and gained results are demonstrated. The paper definitely has many good and fruitful ideas, but the mutual interaction and influences of the control section and wireless system, somehow, are vague. Online Computing Reviews Service

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cover image ACM Conferences
ICCPS '16: Proceedings of the 7th International Conference on Cyber-Physical Systems
April 2016
291 pages

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IEEE Press

Publication History

Published: 11 April 2016

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Author Tags

  1. control
  2. cyber-physical system
  3. process control
  4. routing
  5. wireless sensor-actuator network

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Overall Acceptance Rate 25 of 91 submissions, 27%

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Cited By

View all
  • (2019)Age-of-information vs. value-of-information scheduling for cellular networked control systemsProceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems10.1145/3302509.3311050(109-117)Online publication date: 16-Apr-2019
  • (2019)Feedback control goes wirelessProceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems10.1145/3302509.3311046(97-108)Online publication date: 16-Apr-2019
  • (2019)Sampling rate optimization for IEEE 802.11 wireless control systemsProceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems10.1145/3302509.3311045(87-96)Online publication date: 16-Apr-2019
  • (2019)Optimal dynamic scheduling of wireless networked control systemsProceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems10.1145/3302509.3311040(77-86)Online publication date: 16-Apr-2019
  • (2019)Cracking the channel hopping sequences in IEEE 802.15.4e-based industrial TSCH networksProceedings of the International Conference on Internet of Things Design and Implementation10.1145/3302505.3310075(130-141)Online publication date: 15-Apr-2019
  • (2018)Dynamic Wireless Network Reconfiguration for Control System applied to a Nuclear Reactor Case StudyProceedings of the 26th International Conference on Real-Time Networks and Systems10.1145/3273905.3273912(30-40)Online publication date: 10-Oct-2018
  • (2018)Holistic Cyber-Physical Management for Dependable Wireless Control SystemsACM Transactions on Cyber-Physical Systems10.1145/31855103:1(1-25)Online publication date: 5-Sep-2018

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