MPC-based Co-design of Control and Routing for Wireless Sensor and Actuator Networks

Article
  • 2 Downloads

Abstract

A wireless sensor and actuator network (WSAN) is a class of networked control systems. In WSANs, sensors and actuators are located in a distributed way, and communicate to controllers through a wireless communication network such as a multi-hop network. In this paper, we propose a model predictive control (MPC) method for co-design of control and routing of WSANs. MPC is an optimal control strategy based on numerical optimization. The control input is calculated by solving the finite-time optimal control problem at each discrete time. In the proposed method, a WSAN is modeled by a switched linear system. In the finite-time optimal control problem, a control input and a mode corresponding to a communication path are optimized simultaneously. The proposed method is demonstrated by a numerical example.

Keywords

Co-design of control and routing mixed logical dynamical system model predictive control wireless sensor and actuator networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    C. T. Abdallah and H. G. Tanner, “Complex networked control systems: Introduction to the special section,” IEEE Control Systems Magazine, vol. 27, no. 4, pp. 30–32, 2007. [click]CrossRefGoogle Scholar
  2. [2]
    P. Antsaklis and J. Baillieul, “Special issue on technology of networked control systems,” Proc. of the IEEE, vol. 95, no. 1, pp. 5–8, 2007.CrossRefGoogle Scholar
  3. [3]
    R. Alur, A. D’Innocenzo, K. H. Johansson, G. J. Pappas, and G. Weiss, “Compositional modeling and analysis of multi-hop control networks,” IEEE Trans. on Automatic Control, vol. 56, no. 10, pp. 2345–2357, 2011. [click]CrossRefMATHMathSciNetGoogle Scholar
  4. [4]
    B. Radunovi´c and J.-Y. Le Boudec, “Rate performance objectives of multihop wireless networks,” IEEE Trans. on Mobile Computing, vol. 3, no. 4, pp. 334–349, 2004. [click]CrossRefGoogle Scholar
  5. [5]
    I. Saha, L. K. Sambasivan, S. K. Ghosh, and R. K. Patro, “Distributed fault-tolerant topology control in wireless multi-hop networks,” Wireless Networks, vol. 16, no. 6, pp. 1511–1524, 2010. [click]CrossRefGoogle Scholar
  6. [6]
    X. Cao, J. Chen, Y. Xiao, and Y. Sun, “Buildingenvironment control with wireless sensor and actuator networks: centralized versus distributed,” IEEE Trans. on Industrial Electronics, vol. 57, no. 11, pp. 3596–3605, 2011. [click]Google Scholar
  7. [7]
    J. Chen, X. Cao, P. Cheng, Y. Xiao, and Y. Sun, “Distributed collaborative control for industrial automation with wireless sensor and actuator networks,” IEEE Trans. on Industrial Electronics, vol. 57, no. 12, pp. 4219–4230, 2010.CrossRefGoogle Scholar
  8. [8]
    P. Gil, A. Paulo, L. Palma, A. Amâncio, and A. Cardoso, “Model based predictive control over wireless sensor and actuator networks,” Proc. of the 37th Annual Conf. of the IEEE Industrial Electronic Society, pp. 2600–2605, 2011.Google Scholar
  9. [9]
    M. B. Kane, J. Scruggs, and J. P. Lynch, “Model-predictive control techniques for hydronic systems implemented on wireless sensor and actuator networks,” Proc. of the 2014 American Control Conf., pp. 3542–3547, 2014. [click]CrossRefGoogle Scholar
  10. [10]
    S. A. Kumar and K. T. F. Simonsen, “Towards a modelbased development approach for wireless sensor-actuator network protocols,” Proc. of the 4th ACM SIGBED Int’l Workshop on Design, Modeling, and Evaluation of Cyber-Physical Systems, pp. 35–39, 2014.CrossRefGoogle Scholar
  11. [11]
    B. Li, Y. Ma, T. Westenbroek, C. Wu, H. Gonzalez, and C. Lu, “Wireless routing and control: a cyber-physical case study,” Proc. of the ACM/IEEE 7th Int’l Conf. on Cyber-Physical Systems, 2016.Google Scholar
  12. [12]
    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,” Proccedings of the IEEE, vol. 104, no. 5, pp. 1013–1024, 2016. [click]CrossRefGoogle Scholar
  13. [13]
    A. E.-D. Mady and G. Provan, “Co-design of wireless sensor-actuator networks for building controls,” Proc. of the IEEE Conf. on Decision and Control and European Control Conf., pp. 5266–5273, 2011.CrossRefGoogle Scholar
  14. [14]
    D. Martínez, F. Blanes, J. Simo, and A. Crespo, “Wireless sensor and actuator networks: characterization and case study for confined spaces healthcare applications,” Proc. of the Int’l Multiconf. on Computer Science and Information Technology, pp. 687–693, 2008.Google Scholar
  15. [15]
    M. Mazo, Jr. and P. Tabuada, “Decentralized eventtriggered control over wireless sensor/actuator networks,” IEEE Trans. on Automatic Control, vol. 56, no. 10, pp. 2456–2461, 2011. [click]CrossRefMATHMathSciNetGoogle Scholar
  16. [16]
    H. Nakayama, Z. Md. Fadlullah, N. Ansari, and N. Kato, “A novel scheme for WSAN sink mobility based on clustering and set packing techniques,” IEEE Trans. on Automatic Control, vol. 56, no. 10, pp. 2381–2389, 2011.CrossRefMathSciNetGoogle Scholar
  17. [17]
    R. Verdone, D. Dardari, G. Mazzini, and A. Conti, Wireless Sensor and Actuator Networks: Technologies, Analysis and Design, Academic Press, 2010.Google Scholar
  18. [18]
    F. Xia, Y.-C. Tia, Y. Li, and Y. Sun, “Wireless sensor/ actuator network design for mobile control applications,” Sensors, vol. 7, no. 10, pp. 2157–2173, 2007. [click]CrossRefGoogle Scholar
  19. [19]
    F. Xia, X. Kong, and Z. Xu, “Cyber-physical control over wireless sensor and actuator networks with packet loss,” Wireless Networking Based Control, S. K. Mazumder Ed., Springer, 2011.Google Scholar
  20. [20]
    L.-W. Yeh, C.-Y. Lu, C.-W. Kou, Y.-C. Tseng, and C.-W. Yi, “Autonomous light control by wireless sensor and actuator networks,” IEEE Sensor Journal, vol. 10, no. 6, pp. 1029–1041. 2010.CrossRefGoogle Scholar
  21. [21]
    E. F. Camacho and C. B. Alba, Model Predictive Control, Springer, 2008.Google Scholar
  22. [22]
    A. Bemporad and M. Morari, “Control of systems integrating logic, dynamics, and constraints,” Automatica, vol. 35, no. 3, pp. 407–427, 1999. [click]CrossRefMATHMathSciNetGoogle Scholar
  23. [23]
    S. Bououden, M. Chadli, L. Zhang, and T. Yang, “Constrained model predictive control for time-varying delay systems: Application to an active car suspension,” Int’l Journal of Control, Automation and Systems, vol. 14, no. 1, pp. 51–58, 2016.CrossRefGoogle Scholar
  24. [24]
    S. Bououden, M. Chadli, and H. R. Karimi, “A robust predictive control design for nonlinear active suspension systems,” Asian Journal of Control, vol. 18, no. 1, pp. 122–132, 2016.CrossRefMATHMathSciNetGoogle Scholar
  25. [25]
    D. Q. Mayne, M. M. Seron, and S. V. Rakovi´c, “Robust model predictive control of constrained linear systems with bounded disturbances,” Automatica, vol. 41, no. 2, pp. 219–224, 2005. [click]CrossRefMATHMathSciNetGoogle Scholar
  26. [26]
    S. Bououden, M. Chadli, A. Fouad, and S. Filali, “A new approach for fuzzy predictive adaptive controller design using particle swarm optimization algorithm,” Int’l Journal of Innovative Computing, Information & Control, vol. 9, no. 9, pp. 3741–3758, 2013.Google Scholar
  27. [27]
    S. Bououden, M. Chadli, and H. R. Karimi, “An ant colony optimization-based fuzzy predictive control approach for nonlinear processes,” Information Sciences, vol. 299, pp. 143–158, 2015. [click]CrossRefMATHMathSciNetGoogle Scholar
  28. [28]
    Y. Xu, R. Lu, H. Peng, K. Xie, and A. Xue, “Asynchronous dissipative state estimation for stochastic complex networks with quantized jumping coupling and uncertain measurements,” IEEE Trans. on Neural Networks and Learning Systems, vol. 28, no. 2, pp. 268–277, 2017. [click]CrossRefMathSciNetGoogle Scholar
  29. [29]
    Y. Xu, R. Lu, P. Shi, J. Tao, and S. Xie, “Robust estimation for neural networks with randomly occurring distributed delays and Markovian jump coupling,” IEEE Trans. on Neural Networks and Learning Systems, available online.Google Scholar
  30. [30]
    S. Han, Z. Zhong, H. Li, G. Chen, E. Chan, and A. K. Mok, “Coding-aware multi-path routing in multi-hop wireless networks,” Proc. of the IEEE Int’l Performance Computing and Communications Conf. pp. 93–100, 2008.Google Scholar
  31. [31]
    H. Okada, N. Nakagawa, T. Wada, T. Yamazato, and M. Katayama, “Multi-route coding in wireless multi-hop networks,” IEICE Trans. on Communications, vol. E89-B, pp. 1620–1626, 2006.CrossRefGoogle Scholar
  32. [32]
    H. Okada, T. Wada, K. Ohuchi, M. Saito, T. Yamazato, and M. Katayama, “Throughput evaluation of ARQ scheme for multi-route coding in wireless multi-hop networks,” Proc. of the 2006 IEEE 63rd Vehicular Technology Conf., pp. 668–672, 2006.CrossRefGoogle Scholar
  33. [33]
    M. C. F. Donkers, W. P. M. H. Heemels, N. van de Wouw, and L. Hetel, “Stability analysis of networked control systems using a switched linear systems approach,” IEEE Trans. on Automatic Control, vol. 56, no. 9, pp. 2101–2115, 2011. [click]CrossRefMATHMathSciNetGoogle Scholar
  34. [34]
    K. Kobayashi and K. Hiraishi, “Optimal control of multihop control networks based on the MLD framework,” IEEJ Trans. on Electrical and Electronic Engineering, vol. 10, no. 6, pp. 699–705, 2015.CrossRefGoogle Scholar
  35. [35]
    I. F. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: a survey,” Computer Networks, vol. 47, no. 4, pp. 445–487, 2005. [click]CrossRefMATHGoogle Scholar
  36. [36]
    W. P. M. H. Heemels, K. H. Johansson, and P. Tabuada, “An introduction to event-triggered and self-triggered control,” Proc. of the 51st IEEE Conf. on Decision and Control, pp. 3270–3285, 2012.Google Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan

Personalised recommendations