Skip to main content
Log in

Adaptive PID controller for cloud smart city system stability control based on chaotic neural network

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Stability is an essential component in system control especially with those systems exhibiting a non-linear dynamic behaviour. PID controllers are an integral and most commonly used tuning method for control over non-linear systems due to their simplicity and ease of computational complexity. PID controllers are most useful to systems in real time in which the system control parameters vary very slowly or exhibit uncertainty consequently making prediction very complicated and delayed. Continuous and quick adaptation of PID controllers towards varying input determines the efficiency of the non-linear system and this research paper presents a radial basis neural network model in a chaotic configuration for tuning the PID controller in an adaptive manner. However, because mobile cloud computing inherits some characteristics of mobile computing itself, there may be problems such as network congestion, no signal area and crosstalk, which leads to the frequent failure of computing resources and service cooperation, which makes the availability of mobile cloud computing more difficult than traditional cloud calculate the more severe challenges. The scope of this research work is limited to training the neural network with a chaotic map and time series plots are obtained using the experimental results. The efficiency of the proposed chaotic network model is justified by the evaluation of accuracy and robustness of the model even in presence of disturbances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Prakash, R., Anita, R.: Robust model reference adaptive PI control. J. Theor. Appl. Inf. Technol. 14(1), 51–59 (2010)

    Google Scholar 

  2. Antsaklisz, P., Baillieul, J.: Special Issue on networked control systems. IEEE Trans. Autom. Control 49(9), 1421–1423 (2004)

  3. Falahien, R., Dastjerdi, M.M., Gharibzadeh, S.: Pragmatic modelling of chaotic dynamic systems through artificial neural networks. In: proceedings of 21st International Conference on Biomedical Engineering, pp. 103–108 (2014)

  4. Chang, W.D., Hwang, R.C., Hsieh, J.G.: A self-tuning PID control for a class of nonlinear systems based on the Lyapunov approach. J. Process Control 12, 233–242 (2002)

    Article  Google Scholar 

  5. Altinten, A., Erdogan, S., Alioglu, F., Hapoglu, B., Alpbaz, M.: Application of adaptive PID control with genetic algorithm to a polymerization reactor. Chem. Eng. Commun. 191, 1158–1172 (2004)

    Article  Google Scholar 

  6. Cominos, P., Munro, N.: PID controllers: recent tuning methods and design to specification. IEEE Proc. Control Theory Appl. 149, 46–53 (2002)

    Article  Google Scholar 

  7. Song, G., Chaudhry, V., Batur, C.: Precision tracking control of shape memory alloy actuators using neural networks and a sliding mode based robust controller. Smart Mater. Struct. 12, 223 (2003)

    Article  Google Scholar 

  8. Song, X., Wang, X.: New agent-based proactive migration method and system for big data environment (BDE). Eng. Comput. 32(8), 2443–2466 (2015)

    Article  Google Scholar 

  9. Vallabhai, M.J., Swarnkar, P., Deshpande, D.M.: Comparative analysis of Pi control and model reference adaptive control based vector control strategy for induction motor drive. Int. J. Eng. Res. Appl. 2(3), 2059–2070 (2012)

    Google Scholar 

  10. Ho, W.K., Lee, T.H., Han, H.P., Hong, Y.: Self-tuning IMC-PID controller with gain and phase margins assignment. IEEE Trans. Control Syst. Technol. 9(3), 535–541 (2001)

    Article  Google Scholar 

  11. Ordonez, R., Passino, K.M.: Adaptive control for a class of nonlinear systems with a time varying structure. IEEE Trans. Autom. Control 46(1), 152–155 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  12. Arora, A., Hote, Y., Rastogi, M.: Design of PID controller for unstable system control, computation and information systems. In: Balasubramaniam, P. (ed.) Control, Computation and Information Systems, pp. 19–26. Springer, Berlin, Germany (2011)

    Chapter  Google Scholar 

  13. Zhang, D., Li, G., Zheng, K.: An energy-balanced routing method based on forward-aware factor for wireless sensor network. IEEE Trans. Ind. Inform. 10(1), 766–773 (2014)

    Article  Google Scholar 

  14. Zhang, D., Wang, X., Song, X.: A novel approach to mapped correlation of ID for RFID anti-collision. IEEE Trans. Serv. Comput. 7(4), 741–748 (2014)

    Article  Google Scholar 

  15. Bhardwaj, A., Goundar, S.: Designing a framework for cloud service agreement for cloud environments. Int. J. Cloud Appl. Comput. (IJCAC) 6(4), 83–96 (2016)

    Google Scholar 

  16. Gupta, B.B., Gupta, S., Chaudhary, P.: Enhancing the browser-side context-aware sanitization of suspicious HTML5 code for halting the DOM-based XSS vulnerabilities in cloud. Int. J. Cloud Appl. Comput. (IJCAC) 7(1), 1–31 (2017)

    Google Scholar 

  17. Liang, Y.: A kind of novel method of service-aware computing for uncertain mobile applications. Math. Comput. Model. 57(3–4), 344–356 (2013)

    Google Scholar 

  18. Wang, J., Wang, H., Zhou, Y., McDonald, N.: Multiple kernel multivariate performance learning using cutting plane algorithm. In: 2015 IEEE International Conference on Systems, man, and cybernetics (SMC), pp. 1870–1875. IEEE (2015)

  19. Sangaiah, A.K., Samuel, O.W., Li, X., Abdel-Basset, M., Wang, H.: Towards an efficient risk assessment in software projects-Fuzzy reinforcement paradigm. Comput. Electr. Eng. (2017). doi:10.1016/j.compeleceng.2017.07.022

    Article  Google Scholar 

  20. Zheng, K., Zhang, T.: A novel multicast routing method with minimum transmission for WSN of cloud computing service. Soft Comput. 19(7), 1817–1827 (2015)

    Article  Google Scholar 

  21. Zhang, X.: Design and implementation of embedded un-interruptible power supply system (EUPSS) for web-based mobile application. Enterp. Inf. Syst. 6(4), 473–489 (2012)

    Article  Google Scholar 

  22. Shukla, N., Tiwari, A.: An empirical investigation of using ANN based N-state sequential machine and chaotic neural network in the field of cryptography. Glob. J. Comput. Sci. Technol. Neural Artif. Intell. 12(10 1), 17–26 (2012)

  23. Wang, H., Wang, J.: An effective image representation method using kernel classification. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 853–858. IEEE (2014)

  24. Bi, C., Wang, H., Bao, R.: SAR image change detection using regularized dictionary learning and fuzzy clustering. In: 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 327–330. IEEE (2014)

  25. Sheikholeslami, K.A.: A survey of chaos embedded meta heuristic algorithms. Int. J. optim. Civ. Eng. 3(4), 617–633 (2013)

    Google Scholar 

  26. Vichai, S., Hirai, S., Komura, M., Kuroki, S.: Hybrid control based model reference adaptive control. Elektrotechnika 3(59), 5–8 (2005)

    Google Scholar 

  27. Alatas, B.: Chaotic bee colony algorithms for global numerical optimization. Expert Syst. Appl. 37, 5682–5687 (2010)

    Article  Google Scholar 

  28. Tipsuwan, Y., Chow, M.Y.: Control methodologies in networked control systems. Control Eng. Pract. 11(10), 1099–1111 (2003)

    Article  Google Scholar 

  29. Lee, K.C., Lee, S.: Implementation of networked control system using a profibus-DP network. Int. J. Korean Soc. Precis. Eng. 3(3), 12–20 (2002)

    Google Scholar 

  30. Xu, L., Yao, B.: Output feedback adaptive robust precision motion control of linear motors. Automatica 37(7), 1029–39 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhang, D.: A new approach and system for attentive mobile learning based on seamless migration. Appl. Intell. 36(1), 75–89 (2012)

    Article  Google Scholar 

  32. Zhu, Y.: A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the Internet of Things (IOT). Comput. Math. Appl. 64(5), 1044–1055 (2012)

    Article  MATH  Google Scholar 

  33. Bakshi, S., Sa, P.K., Wang, H., Barpanda, S.S., Majhi, B.: Fast periocular authentication in handheld devices with reduced phase intensive local pattern. Multimed. Tools Appl. (2017). doi:10.1007/s11042-017-4965-6

    Article  Google Scholar 

  34. Zheng, K., Zhao, D.: Novel quick start (QS) method for optimization of TCP. Wirel. Netw. 22(1), 211–222 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

Funding was provided by National Natural Science Foundation of China (Grant No. 51475338).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xuebiao Zhu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, X., Chen, K., Wang, Y. et al. Adaptive PID controller for cloud smart city system stability control based on chaotic neural network. Cluster Comput 22 (Suppl 6), 13067–13075 (2019). https://doi.org/10.1007/s10586-017-1197-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1197-5

Keywords

Navigation