Personal and Ubiquitous Computing

, Volume 20, Issue 3, pp 361–372 | Cite as

A laguerre neural network-based ADP learning scheme with its application to tracking control in the Internet of Things

  • Xiong Luo
  • Yixuan Lv
  • Mi Zhou
  • Weiping Wang
  • Wenbing Zhao
Original Article

Abstract

Sensory data have becoming widely available in large volume and variety due to the increasing presence and adoption of the Internet of Things. Such data can be tremendously useful if they are processed properly in a timely fashion. They could play a key role in the coordination of industrial production. It is thus desirable to explore an effective and efficient scheme to support data tracking and monitoring. This paper intends to propose a novel automatic learning scheme to improve the tracking efficiency while maintaining or improving the data tracking accuracy. A core strategy in the proposed scheme is the design of Laguerre neural network (LaNN)-based approximate dynamic programming (ADP). As a traditional optimal learning strategy, ADP is a popular approach for data processing. The action neural network (NN) and the critic NN as two important components in ADP have big impact on the performance of ADP. In this paper, a LaNN is employed as the implementation of the action NN in ADP considering Laguerre polynomials’ approximation capability. In addition, this LaNN-based ADP is integrated into an online parameter-tuning framework to optimize those parameters of characteristic model that is used to trace the data in the tracking control system. Meanwhile, this article provides an associated Lyapunov convergence analysis to guarantee a uniformly ultimately boundedness property for tracking errors in the proposed approach. Furthermore, the proposed LaNN-based ADP optimal online parameter-tuning scheme is validated using a temperature dynamic tracking control task. The simulation results demonstrate that the scheme has satisfactory learning performance over time.

Keywords

Automatic tracking Approximate dynamic programming (ADP) Laguerre neural network Characteristic model Parameter tuning Internet of Things 

References

  1. 1.
    Sun YC, Bie RF, Thomas P, Cheng XZ (2015) Theme issue on advances in the internet of things: identification, information, and knowledge. Pers Ubiquitous Comput 19(7):985–987CrossRefGoogle Scholar
  2. 2.
    Carretero J, Garcia JD (2014) The Internet of Things: connecting the world. Pers Ubiquitous Comput 18(2):445–447CrossRefGoogle Scholar
  3. 3.
    Zhou ZB, Tang J, Zhang LJ, Ning K, Wang Q (2014) EGF-tree: an energy-efficient index tree for facilitating multi-region query aggregation in the internet of things. Pers Ubiquitous Comput 18(4):951–966CrossRefGoogle Scholar
  4. 4.
    Guo JQ, Zhang HY, Sun YC, Bie RF (2014) Square-root unscented Kalman filtering-based localization and tracking in the internet of things. Pers Ubiquitous Comput 18(4):987–996CrossRefGoogle Scholar
  5. 5.
    Barthel R, Mackley KL, Hudson-Smith A, Karpovich A, de Jode M, Speed C (2013) An internet of old things as an augmented memory system. Pers Ubiquitous Comput 17(2):321–333CrossRefGoogle Scholar
  6. 6.
    Forsstrom S, Kanter T (2014) Enabling ubiquitous sensor-assisted applications on the internet-of-things. Pers Ubiquitous Comput 18(4):977–986CrossRefGoogle Scholar
  7. 7.
    Lopez TS, Ranasinghe DC, Harrison M, McFarlane D (2012) Adding sense to the internet of things an architecture framework for smart objective systems. Pers Ubiquitous Comput 16(3):291–308CrossRefGoogle Scholar
  8. 8.
    Zhang DQ, Zhao SJ, Yang LT, Chen M, Wang YS, Liu HZ (2015) NextMe: localization using cellular traces in internet of things. IEEE Trans Ind Inf 11(2):302–312Google Scholar
  9. 9.
    Li Z, Wang T, Gong Z, Li N (2013) Forewarning technology and application for monitoring low temperature disaster in solar greenhouses based on internet of things. Trans Chin Soc Agric Eng 29(4):229–236Google Scholar
  10. 10.
    Vastamaki R, Sinkkonen I, Leinonen C (2005) A behavioural model of temperature controller usage and energy saving. Pers Ubiquitous Comput 9(4):250–259CrossRefGoogle Scholar
  11. 11.
    Bellman R, Dreyfus S (1962) Applied dynamic programming. Princeton University Press, PrincetionCrossRefMATHGoogle Scholar
  12. 12.
    Yin B, Dridi M, Moudni AE (2015) Forward search algorithm based on dynamic programming for real-time adaptive traffic signal control. IET Intel Transp Syst 9(7):754–764CrossRefGoogle Scholar
  13. 13.
    Si J, Wang YT (2001) On-line learning control by association and reinforcement. IEEE Trans Neural Netw 12(2):264–276MathSciNetCrossRefGoogle Scholar
  14. 14.
    He H, Ni Z, Fu J (2012) A three-network architecture for on-line learning and optimization based on adaptive dynamic programming. Neurocomputing 78(1):3–13CrossRefGoogle Scholar
  15. 15.
    Modares H, Lewis FL, Naghibi-Sistani MB (2013) Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks. IEEE Trans Neural Netw Learn Syst 24(10):1513–1525CrossRefGoogle Scholar
  16. 16.
    Lewis FL, Liu DR (2012) Reinforcement learning and approximate dynamic programming for feedback control. Wiley-IEEE, HobokenCrossRefGoogle Scholar
  17. 17.
    Pao YH, Philips SM (1995) The functional link net and learning optimal control. Neurocomputing 9(2):149–164CrossRefMATHGoogle Scholar
  18. 18.
    Zhang H, Chen Z, Wang Y, Li M, Qin T (2006) Adaptive predictive control algorithm based on laguerre functional model. Int J Adapt Control Signal Process 20(2):53–76MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Patra JC, Meher PK, Chakraborty G (2011) Development of laguerre neural-network-based intelligent sensors for wireless sensor networks. IEEE Trans Instrum Meas 60(3):725–734CrossRefGoogle Scholar
  20. 20.
    Wu HX, Hu J, Xie YC (2007) Characteristic model-based all-coefficient adaptive control method and its applications. IEEE Trans Syst Man Cybern Pt C Appl Rev 37(2):213–221CrossRefGoogle Scholar
  21. 21.
    Luo X, Liu F, Sun FC (2014) Attitude tracking control for hypersonic vehicles based on type-2 fuzzy dynamic characteristic modeling method. In: Proceedings of the IEEE international conference on fuzzy systems. IEEE, Piscataway, pp 113–120Google Scholar
  22. 22.
    Luo X, Sun ZQ, Sun FC (2009) A new approach to fuzzy modeling and control for nonlinear dynamic systems: neuro-fuzzy dynamic characteristic modeling and adaptive control mechanism. Int J Control Autom Syst 7(1):123–132CrossRefGoogle Scholar
  23. 23.
    Luo X, Li J (2011) Fuzzy dynamic characteristic model based attitude control of hypersonic vehicle in gliding phase. Sci China Inf Sci 54(3):448–459MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Luo X, Luo H, Chang XH (2015) Online optimization of collaborative web service QoS prediction based on approximate dynamic programming. Int J Distrib Sens Netw 2015:1–9MathSciNetGoogle Scholar
  25. 25.
    Haykin SO (2008) Neural networks and learning machines, 3rd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  26. 26.
    Patra JC, Bornand C, Meher PK (2009) Laguerre neural network-based smart sensors for wireless sensor network. In: Proceeding of the IEEE international instrumentation and measurement technology conference. Piscataway, IEEE, pp 832–837Google Scholar
  27. 27.
    Attar RE (2006) Special functions and orthogonal polynomials. Lulu Press, MorrisvilleGoogle Scholar
  28. 28.
    Patra JC, Pal RN, Chatterji BN, Panda G (1999) Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans Syst Man Cybern Part B Cybern 29(2):254–262CrossRefGoogle Scholar
  29. 29.
    Si J, Barto AG, Powell WB, Wunsch D (2004) Handbook of learning and approximate dynamic programming: scaling up to the real world. Wiley-IEEE, PiscatawayCrossRefGoogle Scholar
  30. 30.
    Ryzhov IO, Frazier PI, Powell WB (2015) A new optimal stepsize for approximate dynamic programming. IEEE Trans Autom Control 60(3):743–758MathSciNetCrossRefGoogle Scholar
  31. 31.
    Sun J, Liu F, Si J, Mei S (2012) Direct heuristic dynamic programming based on an improved PID neural network. J Control Theory Appl 10(4):497–503MathSciNetCrossRefGoogle Scholar
  32. 32.
    Liu F, Sun J, Si J, Guo W, Mei S (2012) A boundedness result for the direct heuristic dynamic programming. Neural Netw 32:229–235CrossRefMATHGoogle Scholar
  33. 33.
    Wang LJ (2011) DRNN based self-adjusting of all-coefficients adaptive controller and identification of its characteristics parameters. Aerosp Control 29(5):15–21Google Scholar
  34. 34.
    Wu HX, Wang Y, Xie YC (2002) Characteristic modeling and control of nonlinear systems. Control Eng China 9(6):1–7Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Xiong Luo
    • 1
    • 2
  • Yixuan Lv
    • 1
    • 2
  • Mi Zhou
    • 1
    • 2
  • Weiping Wang
    • 1
    • 2
  • Wenbing Zhao
    • 3
  1. 1.School of Computer and Communication EngineeringUniversity of Science and Technology Beijing (USTB)BeijingChina
  2. 2.Beijing Key Laboratory of Knowledge Engineering for Materials ScienceBeijingChina
  3. 3.Department of Electrical Engineering and Computer ScienceCleveland State UniversityClevelandUSA

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