Full Text:   <1039>

Summary:  <384>

CLC number: TU391; TU392.5

On-line Access: 2016-04-05

Received: 2015-05-19

Revision Accepted: 2015-11-30

Crosschecked: 2016-03-08

Cited: 0

Clicked: 1602

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Peng-cheng Yang

http://orcid.org/0000-0002-5149-7622

Yao-zhi Luo

http://orcid.org/0000-0002-9484-775X

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2016 Vol.17 No.4 P.253-272

http://doi.org/10.1631/jzus.A1500109


Active structures integrated with wireless sensor and actuator networks: a bio-inspired control framework


Author(s):  Peng-cheng Yang, Yan-bin Shen, Yao-zhi Luo

Affiliation(s):  Zhejiang Provincial Key Laboratory of Space Structures, Zhejiang University, Hangzhou 310058, China

Corresponding email(s):   luoyz@zju.edu.cn

Key Words:  Active structures (AS), Wireless sensor and actuator networks (WSAN), Shape control, Bio-inspired control


Share this article to: More |Next Article >>>

Peng-cheng Yang, Yan-bin Shen, Yao-zhi Luo. Active structures integrated with wireless sensor and actuator networks: a bio-inspired control framework[J]. Journal of Zhejiang University Science A, 2016, 17(4): 253-272.

@article{title="Active structures integrated with wireless sensor and actuator networks: a bio-inspired control framework",
author="Peng-cheng Yang, Yan-bin Shen, Yao-zhi Luo",
journal="Journal of Zhejiang University Science A",
volume="17",
number="4",
pages="253-272",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1500109"
}

%0 Journal Article
%T Active structures integrated with wireless sensor and actuator networks: a bio-inspired control framework
%A Peng-cheng Yang
%A Yan-bin Shen
%A Yao-zhi Luo
%J Journal of Zhejiang University SCIENCE A
%V 17
%N 4
%P 253-272
%@ 1673-565X
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1500109

TY - JOUR
T1 - Active structures integrated with wireless sensor and actuator networks: a bio-inspired control framework
A1 - Peng-cheng Yang
A1 - Yan-bin Shen
A1 - Yao-zhi Luo
J0 - Journal of Zhejiang University Science A
VL - 17
IS - 4
SP - 253
EP - 272
%@ 1673-565X
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1500109


Abstract: 
One of the main problems in controlling the shape of active structures (AS) is to determine the actuations that drive the structure from the current state to the target state. Model-based methods such as stochastic search require a known type of load and relatively long computational time, which limits the practical use of AS in civil engineering. Moreover, additive errors may be produced because of the discrepancy between analytic models and real structures. To overcome these limitations, this paper presents a compound system called WAS, which combines AS with a wireless sensor and actuator network (WSAN). A bio-inspired control framework imitating the activity of the nervous systems of animals is proposed for WAS. A typical example is tested for verification. In the example, a triangular tensegrity prism that aims to maintain its original height is integrated with a WSAN that consists of a central controller, three actuators, and three sensors. The result demonstrates the feasibility of the proposed concept and control framework in cases of unknown loads that include different types, distributions, magnitudes, and directions. The proposed control framework can also act as a supplementary means to improve the efficiency and accuracy of control frameworks based on a common stochastic search.

This paper presents a compound systems called WAS, which is the AS combined with a wirelesses sensor and actuator network (WSAN).

集成无线传感器-执行器网络的主动结构:一种仿生控制框架

目的:主动结构的几何形状控制一直是结构控制领域的研究前沿。为满足形状控制目标,一个主要问题就是如何求解主动构件的执行量。以随机搜索算法为核心的基于模型的控制方法逐渐成为主流,但仍存在若干需改善之处,如计算量大、实时性差、不能很好应对未知荷载作用以及实际结构与数值模型间存在差异等。本研究旨在寻求一种控制框架,使其能够在某些场合下具备更好的控制性能。
创新点:1. 提出一类主动结构混合系统--集成无线传感器-执行器网络的主动结构(WAS);2. 通过模仿动物反射活动以及节律运动,提出WAS的两层级仿生控制框架。
方法:1. 将无线传感器-执行器网络引入主动结构,组成混合系统,建立离散的基本模型(图1);2. 结合仿生思想,按照两层级控制框架编制基本控制流程(图3和4);3. 通过仿真模拟,将无线传感器-执行器网络嵌入主动三棱柱张拉整体结构,运用仿生控制框架对张拉整体结构在多种工况下进行形状控制,验证所提概念和方法的可行性与有效性(图7和13)。
结论:1. 与以随机搜索算法为主的基于模型的形状控制方法相比,本文所提出的混合系统及其仿生控制框架,计算量极小,因此可快速应对外部作用的变化;2. 对于未知荷载作用,本文提出的仿生控制框架无需进行荷载识别,因此适应性更强;3. 由于不依赖于有限元模型,该仿生控制框架避免了来自实际结构与数值模型的误差,因此控制精度更高。

关键词:主动结构;无线传感器-执行器网络;形状控制;仿生控制

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Adam, B., Smith, I.F.C., 2006. Learning, self-diagnosis and multi-objective control of an active tensegrity structure. In: Pandey, M., Xie, W.C., Xu, L. (Eds.), Advances in Engineering Structures, Mechanics & Construction. Springer Netherlands, Dordrecht, the Netherlands, p.439-448.

[2]Adam, B., Smith, I.F.C., 2007a. Self-diagnosis and self-repair of an active tensegrity structure. Journal of Structural Engineering, 133(12):1752-1761.

[3]Adam, B., Smith, I.F.C., 2007b. Tensegrity active control: multiobjective approach. Journal of Computing in Civil Engineering, 21(1):3-10.

[4]Adam, B., Smith, I.F.C., 2008. Active tensegrity: a control framework for an adaptive civil-engineering structure. Computers & Structures, 86(23-24):2215-2223.

[5]Akyildiz, I.F., Kasimoglu, I.H., 2004. Wireless sensor and actor networks: research challenges. Ad Hoc Networks, 2(4):351-367.

[6]Angellier, N., Dubé, J., Quirant, J., et al., 2012. Behavior of a double-layer tensegrity grid under static loading: identification of self-stress level. Journal of Structural Engineering, 139(6):1075-1081.

[7]Atzori, L., Iera, A., Morabito, G., 2010. The internet of things: a survey. Computer Networks, 54(15):2787-2805.

[8]Bathe, K.J., Wilson, E.L., 1976. Numerical Methods in Finite Element Analysis. Prentice-Hall, Upper Saddle River, NJ, USA.

[9]Belytschko, T., Liu, W.K., Moran, B., 2000. Nonlinear Finite Elements for Continua and Structures. Wiley, New York.

[10]Bliss, T.K., Iwasaki, T., Bart-Smith, H., 2008. CPG control of a tensegrity morphing structure for biomimetic applications. Advances in Science and Technology, 58:137-142.

[11]Brown, T.G., 1914. On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system. The Journal of Physiology, 48(1):18-46.

[12]Cao, X.H., Chen, J.M., Xiao, Y., et al., 2010. Building-environment control with wireless sensor and actuator networks: centralized versus distributed. IEEE Transactions on Industrial Electronics, 57(11):3596-3605.

[13]Chen, J., Cao, X., Xiao, Y., et al., 2008. Simulated annealing for optimisation with wireless sensor and actuator networks. Electronics Letters, 44(20):1208-1209.

[14]Chen, J., Cao, X., Cheng, P., et al., 2010. Distributed collaborative control for industrial automation with wireless sensor and actuator networks. IEEE Transactions on Industrial Electronics, 57(12):4219-4230.

[15]Delcomyn, F., 1980. Neural basis of rhythmic behavior in animals. Science, 210(4469):492-498.

[16]Djouadi, S., Motro, R., Pons, J., et al., 1998. Active control of tensegrity systems. Journal of Aerospace Engineering, 11(2):37-44.

[17]Domer, B., Smith, I., 2005. An active structure that learns. Journal of Computing in Civil Engineering, 19(1):16-24.

[18]Domer, B., Fest, E., Lalit, V., et al., 2003a. Combining dynamic relaxation method with artificial neural networks to enhance simulation of tensegrity structures. Journal of Structural Engineering, 129(5):672-681.

[19]Domer, B., Raphael, B., Shea, K., et al., 2003b. A study of two stochastic search methods for structural control. Journal of Computing in Civil Engineering, 17(3):132-141.

[20]Duysens, J., van de Crommert, H.W.A.A., 1998. Neural control of locomotion; Part 1. The central pattern generator from cats to humans. Gait & Posture, 7(2):131-141.

[21]Fest, E., Shea, K., Domer, B., et al., 2003. Adjustable tensegrity structures. Journal of Structural Engineering, 129(4):515-526.

[22]Fest, E., Shea, K., Smith, I., 2004. Active tensegrity structure. Journal of Structural Engineering, 130(10):1454-1465.

[23]Goulding, M., 2009. Circuits controlling vertebrate locomotion: moving in a new direction. Nature Reviews Neuroscience, 10(7):507-518.

[24]IEEE Computer Society, 2011. IEEE Standard for Local and Metropolitan Area Networks—Part 15.4: Low-rate Wireless Personal Area Networks (LR-WPANs). IEEE, New York.

[25]Ijspeert, A.J., 2008. Central pattern generators for locomotion control in animals and robots: a review. Neural Networks, 21(4):642-653.

[26]Kanchanasaratool, N., Williamson, D., 2002. Modelling and control of class NSP tensegrity structures. International Journal of Control, 75(2):123-139.

[27]Kandel, E., 2013. Principles of Neural Science. McGraw-Hill Education, New York.

[28]Kawaguchi, K.I., Hangai, Y., Pellegrino, S., et al., 1996. Shape and stress control analysis of prestressed truss structures. Journal of Reinforced Plastics and Composites, 15(12):1226-1236.

[29]Korkmaz, S., Ali, N.B.H., Smith, I.F.C., 2011. Determining control strategies for damage tolerance of an active tensegrity structure. Engineering Structures, 33(6):1930-1939.

[30]Korkmaz, S., Ali, N.B.H., Smith, I.F.C., 2012. Configuration of control system for damage tolerance of a tensegrity bridge. Advanced Engineering Informatics, 26(1):145-155.

[31]Luo, Y.Z., Yang, C., 2014. A vector-form hybrid particle-element method for modeling and nonlinear shell analysis of thin membranes exhibiting wrinkling. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 15(5):331-350.

[32]Lynch, J.P., 2007. An overview of wireless structural health monitoring for civil structures. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851):345-372.

[33]Miura, K., Furuya, H., 1988. Adaptive structure concept for future space applications. AIAA Journal, 26(8):995-1002.

[34]Morris, R.G.M., Fillenz, M., 2003. Neuroscience: the Science of the Brain. British Neuroscience Association, Liverpool, UK.

[35]Motro, R., 2003. Tensegrity: Structural Systems for the Future. Kogan Page Science, London.

[36]Pavlov, I.P., 1927. Conditioned Reflexes: an Investigation of the Physiological Activity of the Cerebral Cortex. Oxford University Press, Oxford.

[37]Raven, P.H., Johnson, G.B., 2002. Biology. McGraw-Hill, New York.

[38]Shea, K., Fest, E., Smith, I.F.C., 2002. Developing intelligent tensegrity structures with stochastic search. Advanced Engineering Informatics, 16(1):21-40.

[39]Sherrington, C., 1906. The Integrative Action of the Nervous System. Yale University Press, New Haven, USA.

[40]Skelton, R.E., Sultan, C., 1997. Controllable tensegrity: a new class of smart structures. Smart Structures and Materials 1997: Mathematics and Control in Smart Structures, San Diego, CA, USA, p.166-177.

[41]Smith, I.F.C., 2003. From active to intelligent structures. Proceedings of the Seventh International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, Egmond aan Zee, the Netherlands, p.3-4.

[42]Smith, I.F.C., 2009a. Control enhancements of a biomimetic structure. Journal of Information Technology in Construction, 14:229-237.

[43]Smith, I.F.C., 2009b. A control framework for a biomimetic structure. EUROPIA09, Paris, p.153-172.

[44]Soong, T., Manolis, G., 1987. Active structures. Journal of Structural Engineering, 113(11):2290-2302.

[45]Spencer, B.F., Ruiz-Sandoval, M.E., Kurata, N., 2004. Smart sensing technology: opportunities and challenges. Structural Control and Health Monitoring, 11(4):349-368.

[46]Squire, L.R., 2013. Fundamental Neuroscience. Academic Press, San Diego, CA, USA.

[47]Stankovic, J., 2008. When sensor and actuator networks cover the world. ETRI Journal, 30(5):627-633.

[48]Starr, C., Taggart, R., Evers, C., et al., 2008. Biology: the Unity and Diversity of Life. Cengage Learning, Belmont, CA, USA.

[49]Straser, E.G., Kiremidjian, A.S., 1998. A modular, wireless damage monitoring system for structures. Report No. 129, Blume Earthquake Engineering Center, Stanford University, Stanford, CA, USA.

[50]Sultan, C., Skelton, R.E., 1997. Integrated design of controllable tensegrity structures. Adaptive Structures and Material Systems: Proceedings of the 1997 ASME International Mechanical Engineering Congress and Exposition, Dallas, TX, USA, p.27-35.

[51]Swartz, R.A., Lynch, J.P., 2009. Strategic network utilization in a wireless structural control system for seismically excited structures. Journal of Structural Engineering, 135(5):597-608.

[52]Veuve, N.W., Dalil Safaei, S., Smith, I.F.C., 2014. Toward development of a biomimetic tensegrity footbridge. Sixth World Conference on Structural Control and Monitoring, Barcelona, Spain.

[53]Wada, B.K., 1990. Adaptive structures-an overview. Journal of Spacecraft and Rockets, 27(3):330-337.

[54]Wang, Y., Law, K., 2011. Structural control with multi-subnet wireless sensing feedback: experimental validation of time-delayed decentralized H-infinity control design. Advances in Structural Engineering, 14(1):25-39.

[55]Wang, Y., Swartz, R.A., Lynch, J.P., et al., 2007. Decentralized civil structural control using real-time wireless sensing and embedded computing. Smart Structures and Systems, 3(3):321-340.

[56]Wu, Q., Liu, C., Zhang, J., et al., 2009. Survey of locomotion control of legged robots inspired by biological concept. Science in China Series F: Information Sciences, 52(10):1715-1729.

[57]Xu, X., Luo, Y., 2008. Multi-objective shape control of prestressed structures with genetic algorithms. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 222(8):1139-1147.

[58]Xu, X., Luo, Y., 2009. Non-linear displacement control of prestressed cable structures. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 223(7):1001-1007.

[59]Yang, C., Shen, Y.B., Luo, Y.Z., 2014. An efficient numerical shape analysis for light weight membrane structures. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 15(4):255-271.

[60]Yao, J.T., 1972. Concept of structural control. Journal of the Structural Division, 98(7):1567-1574.

[61]Yu, J.Z., Tan, M., Chen, J., et al., 2014. A survey on CPG-inspired control models and system implementation. IEEE Transactions on Neural Networks and Learning Systems, 25(3):441-456.

[62]Zuk, W., 1968. Kinetic structures. Civil Engineering, 38(12):62.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE