Predictive Method of Networked Control of Industrial Automation Systems

Abstract:

A New method for real-time prediction of uncertain network transmission time delay and the closed-loop control method through the network manufacturing and industrial plant are introduced. Put forward delay prediction method is based on the multilayer perceptron neural model. To reduce the amount of the first layer of neurons network, thus reducing the computational burden a real-time implementation, a method for the determination of Markov delay sequence order. Used to predict delay and a zero order equivalent discrete time model of the plant, a kind of time-varying state feedback control algorithm is put forward a strategy to obtain real-time update. The stability of the closed-loop switch sufficient conditions is derived using the theorem of linear system. It shows that the method studies of industrial network by two cases, i.e., a DC motor driven transport roll paper and a milling machine. Simulation study describes the effectiveness of the method to control these challenging problems.

• Periodical: Advanced Materials Research (Volume 1022)
• Pages: 402-405
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1022.402
• Online since: August 2014
• Authors: Hong Mei Lu*, Li Xin Zeng
• Keywords: Delay Prediction, Networked Control System (NCS), Neural Network (NN), Variable Sampling Period

References

[1] De. Pellegrini F, Miorandi . D, Vitturi S, Zanella A On the use of wireless networks at low level of factory automation systems, IEEE Trans Ind Informatics 2(2): 2010，p.129–143.

DOI: 10.1109/tii.2006.872960

[2] Hespanha JP, Naghshtabrizi P, Xu Y A survey of recent results in networked control systems., Proceedings of the IEEE, Special Issue on Technology of Networked Control Systems 95 (1): 2007，p.138–162.

DOI: 10.1109/jproc.2006.887288

[3] Cremean LB, Foote TB, Gillula JH, Hines GH, Kogan D, Kriechbaum KL, Lamb JC, Leibs J, Lindzey L, Rasmussen CE, Stewart AD, Burdick JW, Murray RM Alice: an information-rich autonomous vehicle for high-speed desert navigation., Springer Tracts in Advanced Robotics 36: 2012 , p.437.

DOI: 10.1007/978-3-540-73429-1_14

[4] Zongyu C, Wang LF, Li CX, Liu YH The study of configuration-style CNC system based on CANBUS., Int J Adv Manuf Technol 28(11–12): 2011 , p.1129–1135.

DOI: 10.1007/s00170-004-2456-1

[5] Long YH, Zhou ZD, Liu Q, Chen BY, Zhou HL Embedded-based modular NC systems., Int J Adv Manuf Technol 40(7–8): 2009, p.749–759.

DOI: 10.1007/s00170-008-1384-x

[6] Tang B, Liu GP, Gui WH Improvement of state feedback controller design for networked control systems., IEEE TransCircuits Syst II Express Briefs 55(5): 2013, p.464–468.

DOI: 10.1109/tcsii.2007.914893

[7] Montestruque LA, Antsaklis P Stability of model-based networked control systems with time-varying transmission times., IEEE Trans Automat Contr 49(9): 2012, p.1562–1572.

DOI: 10.1109/tac.2004.834107

[8] Yi J, Wang Q, Zhao D, Wen JT BP neural network prediction-based variable-period sampling approach for networked control systems., Appl Math Comput 185(2): 2010, p.976–988.

DOI: 10.1016/j.amc.2006.07.020

[9] Montestruque LA, Antsaklis P On the model-based control of networked systems., Automatica 39: 2013, p.1837–1843.

DOI: 10.1016/s0005-1098(03)00186-9