1932

Abstract

Networked control systems, where feedback loops are closed over communication networks, arise in several domains, including smart energy grids, autonomous driving, unmanned aerial vehicles, and many industrial and robotic systems active in service, production, agriculture, and smart homes and cities. In these settings, the two main layers of the system, control and communication, strongly affect each other's performance, and they also reveal the interaction between a cyber-system component, represented by information-based computing and communication technologies, and a physical-system component, represented by the environment that needs to be controlled. The information access and distribution constraints required to achieve reliable state estimation and stabilization in networked control systems have been intensively studied over the course of roughly two decades. This article reviews some of the cornerstone results in this area, draws a map for what we have learned over these years, and describes the new challenges that we will face in the future. Rather than simply listing different results, we present them in a coherent fashion using a uniform notation, and we also put them in context, highlighting both their theoreticalinsights and their practical significance. Particular attention is given to recent developments related to decentralized estimation in distributed sensing and communication systems and the information-theoretic value of event timing in the context of networked control.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-control-042820-010811
2023-05-03
2024-04-13
Loading full text...

Full text loading...

/deliver/fulltext/control/6/1/annurev-control-042820-010811.html?itemId=/content/journals/10.1146/annurev-control-042820-010811&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Kim KD, Kumar PR. 2012. Cyber-physical systems: a perspective at the centennial. Proc. IEEE 100:1287–308
    [Google Scholar]
  2. 2.
    Sun Y, Kadota I, Talak R, Modiano E. 2019. Age of Information: A New Metric for Information Freshness Cham, Switz.: Springer
  3. 3.
    Yates RD, Sun Y, Brown DR, Kaul SK, Modiano E, Ulukus S. 2021. Age of information: an introduction and survey. IEEE J. Sel. Areas Commun. 39:1183–210
    [Google Scholar]
  4. 4.
    Yates RD. 2020. The age of information in networks: moments, distributions, and sampling. IEEE Trans. Inf. Theory 66:5712–28
    [Google Scholar]
  5. 5.
    Talak R, Karaman S, Modiano E. 2020. Improving age of information in wireless networks with perfect channel state information. IEEE/ACM Trans. Netw. 28:1765–78
    [Google Scholar]
  6. 6.
    Soleymani T, Baras JS, Hirche S. 2022. Value of information in feedback control: quantification. IEEE Trans. Autom. Control 67:3730–37
    [Google Scholar]
  7. 7.
    Soleymani T, Baras JS, Hirche S, Johansson KH. 2022. Value of information in feedback control: global optimality. IEEE Trans. Autom. Control. In press. https://doi.org/10.1109/TAC.2022.3194125
    [Crossref] [Google Scholar]
  8. 8.
    Uysal E, Kaya O, Ephremides A, Gross J, Codreanu M et al. 2022. Semantic communications in networked systems: a data significance perspective. IEEE Netw. 36:4233–40
    [Google Scholar]
  9. 9.
    Franceschetti M, Minero P 2014. Elements of information theory for networked control systems. Information and Control in Networks G Como, B Bernhardsson, A Rantzer 3–37 Cham, Switz.: Springer
    [Google Scholar]
  10. 10.
    Nair GN, Fagnani F, Zampieri S, Evans RJ. 2007. Feedback control under data rate constraints: an overview. Proc. IEEE 95:108–37
    [Google Scholar]
  11. 11.
    Liberzon D. 2009. Nonlinear control with limited information. Commun. Inform. Syst. 9:41–58
    [Google Scholar]
  12. 12.
    Colonius F, Helmke U, Jordan J, Kawan C, Sailer R, Wirth F 2014. Analysis of networked systems. Control Theory of Digitally Networked Dynamic Systems J Lunze 31–79 Heidelberg, Ger.: Springer
    [Google Scholar]
  13. 13.
    Hespanha JP, Naghshtabrizi P, Xu Y. 2007. A survey of recent results in networked control systems. Proc. IEEE 95:138–62
    [Google Scholar]
  14. 14.
    Yüksel S, Başar T. 2013. Stochastic Networked Control Systems: Stabilization and Optimization Under Information Constraints New York: Birkhäuser
  15. 15.
    Matveev AS, Savkin AV. 2009. Estimation and Control over Communication Networks Boston: Birkhäuser
  16. 16.
    Fang S, Chen J, Ishii H. 2017. Towards Integrating Control and Information Theories Cham, Switz.: Springer
  17. 17.
    Kawan C. 2013. Invariance Entropy for Deterministic Control Systems Cham, Switz.: Springer
  18. 18.
    Fischer T. 1982. Optimal quantized control. IEEE Trans. Autom. Control 27:996–98
    [Google Scholar]
  19. 19.
    Tatikonda S, Sahai A, Mitter S. 2004. Stochastic linear control over a communication channel. IEEE Trans. Autom. Control 49:1549–61
    [Google Scholar]
  20. 20.
    Khina A, Garding ER, Pettersson GM, Kostina V, Hassibi B. 2019. Control over Gaussian channels with and without source-channel separation. IEEE Trans. Autom. Control 64:3690–705
    [Google Scholar]
  21. 21.
    Kostina V, Hassibi B. 2019. Rate-cost tradeoffs in control. IEEE Trans. Autom. Control 64:4525–40
    [Google Scholar]
  22. 22.
    Olfati-Saber R, Fax JA, Murray RM. 2007. Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95:215–33
    [Google Scholar]
  23. 23.
    Nair GN, Evans RJ. 2004. Stabilizability of stochastic linear systems with finite feedback data rates. SIAM J. Control Optim. 43:413–36
    [Google Scholar]
  24. 24.
    Tatikonda S, Mitter SK. 2004. Control under communication constraints. IEEE Trans. Autom. Control 49:1056–68
    [Google Scholar]
  25. 25.
    Hespanha J, Ortega A, Vasudevan L. 2002. Towards the control of linear systems with minimum bit-rate. Proceedings of the 15th International Symposium on Mathematical Theory of Networks and Systems Notre Dame, Ind.: Univ. Notre Dame
    [Google Scholar]
  26. 26.
    Baillieul J. 1999. Feedback designs for controlling device arrays with communication channel bandwidth constraints. ARO Workshop on Smart Structures16–18 Univ. Park: Pa. State Univ.
    [Google Scholar]
  27. 27.
    Baillieul J. 2001. Feedback designs in information-based control. Stochastic Theory and Control B Pasik-Duncan 35–57 Berlin: Springer
    [Google Scholar]
  28. 28.
    Wong WS, Brockett RW. 1997. Systems with finite communication bandwidth constraints. I. State estimation problems. IEEE Trans. Autom. Control 42:1294–99
    [Google Scholar]
  29. 29.
    Wong WS, Brockett RW. 1999. Systems with finite communication bandwidth constraints. II. Stabilization with limited information feedback. IEEE Trans. Autom. Control 44:1049–53
    [Google Scholar]
  30. 30.
    Brockett R, Liberzon D. 2000. Quantized feedback stabilization of linear systems. IEEE Trans. Autom. Control 45:1279–89
    [Google Scholar]
  31. 31.
    Elia N, Mitter SK. 2001. Stabilization of linear systems with limited information. IEEE Trans. Autom. Control 46:1384–400
    [Google Scholar]
  32. 32.
    Liberzon D. 2003. On stabilization of linear systems with limited information. IEEE Trans. Autom. Control 48:304–7
    [Google Scholar]
  33. 33.
    Nair GN, Evans RJ, Mareels IM, Moran W. 2004. Topological feedback entropy and nonlinear stabilization. IEEE Trans. Autom. Control 49:1585–97
    [Google Scholar]
  34. 34.
    Adler RL, Konheim AG, McAndrew MH. 1965. Topological entropy. Trans. Am. Math. Soc. 114:309–19
    [Google Scholar]
  35. 35.
    Shannon CE. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27:379–423
    [Google Scholar]
  36. 36.
    Kolmogorov AN, Tikhomirov VM. 1959. ε-entropy and ε-capacity of sets in function spaces. Uspekhi Mat. Nauk 14:3–86
    [Google Scholar]
  37. 37.
    Lim TJ, Franceschetti M. 2017. Information without rolling dice. IEEE Trans. Inf. Theory 63:1349–63
    [Google Scholar]
  38. 38.
    Donoho D. 2000. Wald Lecture I: counting bits with Shannon and Kolmogorov Tech. Rep. Dep. Stat., Stanford Univ. Stanford, CA:
  39. 39.
    Hagihara R, Nair GN. 2013. Two extensions of topological feedback entropy. Math. Control Signals Syst. 25:473–90
    [Google Scholar]
  40. 40.
    Liberzon D, Hespanha JP. 2005. Stabilization of nonlinear systems with limited information feedback. IEEE Trans. Autom. Control 50:910–15
    [Google Scholar]
  41. 41.
    Sharon Y, Liberzon D. 2012. Input to state stabilizing controller for systems with coarse quantization. IEEE Trans. Autom. Control 57:830–44
    [Google Scholar]
  42. 42.
    De Persis C. 2005. n-bit stabilization of n-dimensional nonlinear systems in feedforward form. IEEE Trans. Autom. Control 50:299–311
    [Google Scholar]
  43. 43.
    Colonius F, Hamzi B. 2021. Entropy for practical stabilization. SIAM J. Control Optim. 59:2195–222
    [Google Scholar]
  44. 44.
    Tatikonda S, Mitter SK. 2004. Control over noisy channels. IEEE Trans. Autom. Control 49:1196–201
    [Google Scholar]
  45. 45.
    Matveev AS, Savkin AV. 2007. An analogue of Shannon information theory for detection and stabilization via noisy discrete communication channels. SIAM J. Control. Optim. 46:1323–67
    [Google Scholar]
  46. 46.
    Sahai A, Mitter SK. 2006. The necessity and sufficiency of anytime capacity for stabilization of a linear system over a noisy communication link—part I: scalar systems. IEEE Trans. Inf. Theory 52:3369–95
    [Google Scholar]
  47. 47.
    Matveev AS, Savkin AV. 2007. Shannon zero error capacity in the problems of state estimation and stabilization via noisy communication channels. Int. J. Control 80:241–55
    [Google Scholar]
  48. 48.
    Minero P, Franceschetti M. 2017. Anytime capacity of a class of Markov channels. IEEE Trans. Autom. Control 62:1356–67
    [Google Scholar]
  49. 49.
    Cover TM, Thomas JA. 2006. Elements of Information Theory Hoboken, NJ: Wiley & Sons. , 2nd ed..
  50. 50.
    Martins NC, Dahleh MA, Elia N 2006. Feedback stabilization of uncertain systems in the presence of a direct link. IEEE Trans. Autom. Control 51:438–47
    [Google Scholar]
  51. 51.
    Minero P, Franceschetti M, Dey S, Nair GN. 2009. Data rate theorem for stabilization over time-varying feedback channels. IEEE Trans. Autom. Control 54:243–55
    [Google Scholar]
  52. 52.
    You K, Xie L. 2010. Minimum data rate for mean square stabilizability of linear systems with Markovian packet losses. IEEE Trans. Autom. Control 56:772–85
    [Google Scholar]
  53. 53.
    Minero P, Coviello L, Franceschetti M. 2013. Stabilization over Markov feedback channels: the general case. IEEE Trans. Autom. Control 58:349–62
    [Google Scholar]
  54. 54.
    Nair GN. 2013. A non-stochastic information theory for communication and state estimation. IEEE Trans. Autom. Control 58:1497–510
    [Google Scholar]
  55. 55.
    Ding J, Peres Y, Ranade G, Zhai A. 2019. When multiplicative noise stymies control. Ann. Appl. Probab. 29:1963–92
    [Google Scholar]
  56. 56.
    Astrom KJ, Bernhardsson BM. 2002. Comparison of Riemann and Lebesgue sampling for first order stochastic systems. Proceedings of the 41st IEEE Conference on Decision and Control, Vol. 22011–16 Piscataway, NJ: IEEE
    [Google Scholar]
  57. 57.
    Tabuada P. 2007. Event-triggered real-time scheduling of stabilizing control tasks. IEEE Trans. Autom. Control 52:1680–85
    [Google Scholar]
  58. 58.
    Khashooei BA, Antunes DJ, Heemels W. 2018. A consistent threshold-based policy for event-triggered control. IEEE Control Syst. Lett 2:447–52
    [Google Scholar]
  59. 59.
    Girard A. 2014. Dynamic triggering mechanisms for event-triggered control. IEEE Trans. Autom. Control 60:1992–97
    [Google Scholar]
  60. 60.
    Wang X, Lemmon MD. 2011. Event-triggering in distributed networked control systems. IEEE Trans. Autom. Control 56:586–601
    [Google Scholar]
  61. 61.
    Dimarogonas DV, Frazzoli E, Johansson KH. 2012. Distributed event-triggered control for multi-agent systems. IEEE Trans. Autom. Control 57:1291–97
    [Google Scholar]
  62. 62.
    Demirel B, Gupta V, Quevedo DE, Johansson M. 2017. On the trade-off between communication and control cost in event-triggered dead-beat control. IEEE Trans. Autom. Control 62:2973–80
    [Google Scholar]
  63. 63.
    Trimpe S, D'Andrea R. 2014. Event-based state estimation with variance-based triggering. IEEE Trans. Autom. Control 59:3266–81
    [Google Scholar]
  64. 64.
    Seuret A, Prieur C, Tarbouriech S, Zaccarian L. 2016. LQ-based event-triggered controller co-design for saturated linear systems. Automatica 74:47–54
    [Google Scholar]
  65. 65.
    Umlauft J, Hirche S. 2019. Feedback linearization based on Gaussian processes with event-triggered online learning. IEEE Trans. Autom. Control 65:4154–69
    [Google Scholar]
  66. 66.
    Maity D, Baras JS. 2019. Optimal event-triggered control of nondeterministic linear systems. IEEE Trans. Autom. Control 65:604–19
    [Google Scholar]
  67. 67.
    Heemels W, Johansson KH, Tabuada P. 2012. An introduction to event-triggered and self-triggered control. 2012 IEEE 51st Conference on Decision and Control3270–85 Piscataway, NJ: IEEE
    [Google Scholar]
  68. 68.
    Tanwani A, Teel A. 2017. Stabilization with event-driven controllers over a digital communication channel with random transmissions. 2017 IEEE 56th Annual Conference on Decision and Control6063–68 Piscataway, NJ: IEEE
    [Google Scholar]
  69. 69.
    Lemmon M. 2010. Event-triggered feedback in control, estimation, and optimization. Networked Control Systems A Bemporad, M Heemels, M Johansson 293–358 London: Springer
    [Google Scholar]
  70. 70.
    Miskowicz M. 2018. Event-Based Control and Signal Processing Boca Raton, FL: CRC
  71. 71.
    Tolić D, Hirche S. 2017. Networked Control Systems with Intermittent Feedback Boca Raton, FL: CRC
  72. 72.
    Tallapragada P, Cortés J. 2015. Event-triggered stabilization of linear systems under bounded bit rates. IEEE Trans. Autom. Control 61:1575–89
    [Google Scholar]
  73. 73.
    Kofman E, Braslavsky JH. 2006. Level crossing sampling in feedback stabilization under data-rate constraints. Proceedings of the 45th IEEE Conference on Decision and Control4423–28 Piscataway, NJ: IEEE
    [Google Scholar]
  74. 74.
    Pearson J, Hespanha JP, Liberzon D. 2017. Control with minimal cost-per-symbol encoding and quasi-optimality of event-based encoders. IEEE Trans. Autom. Control 62:2286–301
    [Google Scholar]
  75. 75.
    Dhulipala AK, Fragouli C, Orlitsky A. 2009. Silence-based communication. IEEE Trans. Inf. Theory 56:350–66
    [Google Scholar]
  76. 76.
    Khojasteh MJ, Tallapragada P, Cortés J, Franceschetti M. 2019. The value of timing information in event-triggered control. IEEE Trans. Autom. Control 65:925–40
    [Google Scholar]
  77. 77.
    Khojasteh MJ, Hedayatpour M, Cortés J, Franceschetti M. 2021. Exploiting timing information in event-triggered stabilization of linear systems with disturbances. IEEE Trans. Control Netw. Syst. 8:15–27
    [Google Scholar]
  78. 78.
    Khojasteh MJ, Tallapragada P, Cortés J, Franceschetti M. 2017. Time-triggering versus event-triggering control over communication channels. 2017 IEEE 56th Annual Conference on Decision and Control5432–37 Piscataway, NJ: IEEE
    [Google Scholar]
  79. 79.
    Khojasteh MJ, Hedayatpour M, Franceschetti M. 2019. Theory and implementation of event-triggered stabilization over digital channels. 2019 IEEE 58th Conference on Decision and Control4183–88 Piscataway, NJ: IEEE
    [Google Scholar]
  80. 80.
    Khojasteh MJ, Franceschetti M, Ranade G. 2018. Stabilizing a linear system using phone calls: when time is information. arXiv:1804.00351 [eess.SY]
  81. 81.
    Anantharam V, Verdú S. 1996. Bits through queues. IEEE Trans. Inf. Theory 42:4–18
    [Google Scholar]
  82. 82.
    Win MZ, Conti A, Mazuelas S, Shen Y, Gifford WM et al. 2011. Network localization and navigation via cooperation. IEEE Commun. Mag. 49:556–62
    [Google Scholar]
  83. 83.
    Conti A, Morselli F, Liu Z, Bartoletti S, Mazuelas S et al. 2021. Location awareness in beyond 5G networks. IEEE Commun. Mag. 59:1111–17
    [Google Scholar]
  84. 84.
    Patwari N, Ash JN, Kyperountas S, Hero AO, Moses RL, Correal NS. 2005. Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process. Mag. 22:454–69
    [Google Scholar]
  85. 85.
    Chiani M, Giorgetti A, Paolini E. 2018. Sensor radar for object tracking. Proc. IEEE 106:1022–41
    [Google Scholar]
  86. 86.
    Bartoletti S, Giorgetti A, Win MZ, Conti A. 2015. Blind selection of representative observations for sensor radar networks. IEEE Trans. Veh. Technol. 64:1388–400
    [Google Scholar]
  87. 87.
    Bartoletti S, Conti A, Giorgetti A, Win MZ. 2014. Sensor radar networks for indoor tracking. IEEE Wirel. Commun. Lett. 3:157–60
    [Google Scholar]
  88. 88.
    Cardone G, Foschini L, Bellavista P, Corradi A, Borcea C et al. 2013. Fostering participaction in smart cities: a geo-social crowdsensing platform. IEEE Commun. Mag. 51:6112–19
    [Google Scholar]
  89. 89.
    Bartoletti S, Conti A, Win MZ. 2017. Device-free counting via wideband signals. IEEE J. Sel. Areas Commun. 35:1163–74
    [Google Scholar]
  90. 90.
    Moreno V, Zamora MA, Skarmeta AF. 2016. A low-cost indoor localization system for energy sustainability in smart buildings. IEEE Sens. J. 16:3246–62
    [Google Scholar]
  91. 91.
    Atzori L, Iera A, Morabito G. 2010. The Internet of Things: a survey. Comput. Netw. 54:2787–805
    [Google Scholar]
  92. 92.
    Win MZ, Meyer F, Liu Z, Dai W, Bartoletti S, Conti A. 2018. Efficient multi-sensor localization for the Internet of Things. IEEE Signal Process. Mag. 35:5153–67
    [Google Scholar]
  93. 93.
    Amadeo M, Campolo C, Quevedo J, Corujo D, Molinaro A et al. 2016. Information-centric networking for the Internet of Things: challenges and opportunities. IEEE Netw. 30:92–100
    [Google Scholar]
  94. 94.
    Win MZ, Shen Y, Dai W. 2018. A theoretical foundation of network localization and navigation. Proc. IEEE 106:1136–65
    [Google Scholar]
  95. 95.
    Win MZ, Dai W, Shen Y, Chrisikos G, Poor HV. 2018. Network operation strategies for efficient localization and navigation. Proc. IEEE 106:1224–54
    [Google Scholar]
  96. 96.
    Conti A, Mazuelas S, Bartoletti S, Lindsey WC, Win MZ. 2019. Soft information for localization-of-things. Proc. IEEE 107:2240–64
    [Google Scholar]
  97. 97.
    Conti A, Guerra M, Dardari D, Decarli N, Win MZ. 2012. Network experimentation for cooperative localization. IEEE J. Sel. Areas Commun. 30:467–75
    [Google Scholar]
  98. 98.
    Savkin AV. 2006. Analysis and synthesis of networked control systems: topological entropy, observability, robustness and optimal control. Automatica 42:51–62
    [Google Scholar]
  99. 99.
    Matveev A, Pogromsky A. 2016. Observation of nonlinear systems via finite capacity channels: constructive data rate limits. Automatica 70:217–29
    [Google Scholar]
  100. 100.
    Liberzon D, Mitra S. 2017. Entropy and minimal bit rates for state estimation and model detection. IEEE Trans. Autom. Control 63:3330–44
    [Google Scholar]
  101. 101.
    Yu S, Chen W, Poor HV 2021. Timing side information aided real-time monitoring of discrete-event systems. 2021 IEEE Global Communications Conference Piscataway, NJ: IEEE https://doi.org/10.1109/GLOBECOM46510.2021.9685563
    [Crossref] [Google Scholar]
  102. 102.
    Liu Z, Conti A, Mitter SK, Win MZ. 2022. Filtering over non-Gaussian channels: the role of anytime capacity. IEEE Control Syst. Lett. 7:472–77
    [Google Scholar]
  103. 103.
    Liu Z, Conti A, Mitter SK, Win MZ. 2021. Networked filtering with feedback for discrete-time observations. 2021 60th IEEE Conference on Decision and Control5882–89 Piscataway, NJ: IEEE
    [Google Scholar]
  104. 104.
    Liu Z, Conti A, Mitter SK, Win MZ. 2022. Networked filtering with feedback for continuous-time observations. 2022 American Control Conference2962–69 Piscataway, NJ: IEEE
    [Google Scholar]
  105. 105.
    Liu Z. 2022. Decentralized inference and its application to network localization and navigation PhD Thesis Mass. Inst. Technol. Cambridge, MA (thesis advisor: Professor Moe Z. Win)
  106. 106.
    Vershynin R. 2018. High-Dimensional Probability: An Introduction with Applications in Data Science Cambridge, UK: Cambridge Univ. Press
  107. 107.
    Mitter SK, Newton NJ. 2005. Information and entropy flow in the Kalman-Bucy filter. J. Stat. Phys. 118:145–76
    [Google Scholar]
  108. 108.
    Newton NJ. 2008. Interactive statistical mechanics and nonlinear filtering. J. Stat. Phys. 133:711–37
    [Google Scholar]
/content/journals/10.1146/annurev-control-042820-010811
Loading
/content/journals/10.1146/annurev-control-042820-010811
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error