Co-simulation of Physical Model and Self-Adaptive Predictive Controller Using Hybrid Automata

  • Imane LamraniEmail author
  • Ayan BanerjeeEmail author
  • Sandeep K. S. GuptaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11176)


Self-adaptive predictive control (SAP) systems adjust their behavior in response to the changing physical system in order to achieve improved control. As such, models of self-adaptive control systems result in time variance of parameters. This significantly increases the complexity of model checking verification and reachability analysis techniques. In this paper, we explore recent studies on co-simulation of SAP controllers and propose a novel co-simulation platform that can be used to analyze the effectiveness of verification and reachability analysis techniques developed for SAP controllers.


Co-simulation Safety verification Hybrid automata Reachability analysis 


  1. 1.
    Jacklin, S., et al.: Verification, validation, and certification challenges for adaptive fight-critical control system software. In: AIAA Guidance, Navigation, and Control Conference and Exhibit (2004)Google Scholar
  2. 2.
    Turksoy, K., Cinar, A.: Adaptive control of artificial pancreas systems-a review. J. Healthc. Eng. 5, 1–22 (2014)CrossRefGoogle Scholar
  3. 3.
    Frehse, G.: Reachability of hybrid systems in space-time. In: ACM SIGBED EMSOFT (2015)Google Scholar
  4. 4.
    Sadeghi, K., et al.: Permanency analysis on human electroencephalogram signals for pervasive brain-computer interface systems. In: 39th Annual International Conference of the IEEE EMBC (2017)Google Scholar
  5. 5.
    Frehse, G.: Scalable verification of hybrid systems. Diss. Univ, Grenoble Alpes (2016)Google Scholar
  6. 6.
    Hovorka, R., et al.: Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol. Measure. 25(4), 905 (2004)CrossRefGoogle Scholar
  7. 7.
    Eren-Oruklu, M., et al.: Self-tuning controller for regulation of glucose levels in patients with type 1 diabetes. In: American Control Conference, pp. 819–824. IEEE (2008)Google Scholar
  8. 8.
    Iftikhar, M.U., Weyns, D.: A case study on formal verification of self-adaptive behaviors in a decentralized system. arXiv preprint arXiv:1208.4635 (2012)
  9. 9.
    Landau, I.D., et al.: Adaptive Control, vol. 51. Springer, New York (1998). Scholar
  10. 10.
    Hatvani, L.: Formal verification of adaptive real-time systems by extending task automata. Diss. Mälardalen University (2014)Google Scholar
  11. 11.
    Chutinan, A., Krogh, B.H.: Computational techniques for hybrid system verification. IEEE Trans. Autom. Control 48(1), 64–75 (2003)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Tan, L.: Model-based self-adaptive embedded programs with temporal logic specifications, pp. 151–158. Software IEEE (2006)Google Scholar
  13. 13.
    Frehse, G., et al.: SpaceEx: scalable verification of hybrid systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 379–395. Springer, Heidelberg (2011). Scholar
  14. 14.
    Larsen, K.G., Pettersson, P., Yi, W.: UPPAAL in a nutshell. Int. J. Softw. Tools Technol. Transf. 1(1–2), 134–152 (1997)CrossRefGoogle Scholar
  15. 15.
    Althoff, M., Le Guernic, C., Krogh, B.H.: Reachable set computation for uncertain time-varying linear systems. In: 14th International Conference on Hybrid Systems: Computation and Control, pp. 93–102. ACM (2011)Google Scholar
  16. 16.
    Andersen, K.E., Højbjerre, M.: A Bayesian approach to Bergman’s minimal model. In: Bishop, C.M., Frey, B.J. (eds.) Ninth International Workshop on Artificial Intelligence (2003)Google Scholar
  17. 17.
    Lamrani, I., et al.: HyMn: mining linear hybrid automata from input output traces of cyber-physical systems. IEEE International Conference on Industrial Cyber-Physical Systems (2018)Google Scholar
  18. 18.
    Moon, I.-H., et al.: Approximate reachability don’t cares for CTL model checking. In: In: IEEE/ACM CAD, pp. 351–358 (1998)Google Scholar
  19. 19.
    Ravi, K., Somenzi, F.: High-density reachability analysis. In: IEEE/ACM CAD, pp. 154–158 (1995)Google Scholar
  20. 20.
    Sadeghi, K., et al.: Optimization of brain mobile interface applications using IoT. In: 23rd International Conference on HiPC. IEEE (2016)Google Scholar
  21. 21.
    Sadeghi, K., et al.: SafeDrive: an autonomous driver safety application in aware cities. In: International Conference on PerCom Workshops. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.iMPACT lab CIDSEArizona State UniversityTempeUSA

Personalised recommendations