Abstract
Time-varying delays adversely affect the performance of networked control systems (NCS) and in the worst case can destabilize the entire system. Therefore, modeling network delays are important for designing NCS. However, modeling time-varying delays are challenging because of their dependence on multiple parameters, such as length, contention, connected devices, protocol employed, and channel loading. Further, these multiple parameters are inherently random and delays vary in a nonlinear fashion with respect to time. This makes estimating random delays challenging. This investigation presents a methodology to model delays in NCS using experiments and general regression neural network (GRNN) due to their ability to capture nonlinear relationship. To compute the optimal smoothing parameter that computes the best estimates, genetic algorithm is used. The objective of the genetic algorithm is to compute the optimal smoothing parameter that minimizes the mean absolute percentage error (MAPE). Our results illustrate that the resulting GRNN is able to predict the delays with less than 3 % error. The proposed delay model gives a framework to design compensation schemes for NCS subjected to time-varying delays.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Seshadhri S, Ayyagari R (2011) Platooning over packet-dropping links. Int J Veh Auton Syst 9(1):46–62
Kato S, Tsugawa S, Tokuda K, Matsui T, Fujii H, Shin Kato et al (2002) Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications. Intell Trans Syst IEEE Trans 33:155–161
Srinivasan S, Ayyagari R (2010) Consensus algorithm for robotic agents over packet dropping links, in Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on, vol 6, no., pp 2636–2640, 16–18 Oct
Raol JR, Gopal A (2010) Mobile intelligent autonomous systems. Defence Sci J 60(1):3–4
Srinivasan S, Ayyagari R (2014) Advanced driver assistance system for AHS over communication links with random packet dropouts. Mech Syst Sig Process 49(1–2), 20 December, 53–62, ISSN 0888-3270
Perumal DG, Saravanakumar G, Subathra B, Seshadhri S, Ramaswmay S (2014) Nonlinear state estimation based predictive path planning algorithm using infrastructure-to-vehicle (I2V) communication for intelligent vehicle. In: Proceedings of the Second international conference on emerging research in computing, information, communication and applications (ERCICA)
Luck R, Ray A (1990) An observer based compensator design for distributed delays. Automatica 26(5):903–908
Srinivasan S, Vallabhan M, Ramaswamy S, Kotta U (2013) Adaptive LQR controller for networked control systems subjected to random communication delays. In: American Control Conference (ACC), pp 783–787, 17–19 June
Nilsson J (1998) Real-time control systems with delays. Ph.D. thesis, Lund Institute of Technology, Lund
Seshadhri S, Ayyagari R (2011) Dynamic controller for Network Control Systems with random communication delay. Int J Syst Control Commun 3:178–193
Cong S, Ge Y, Chen Q, Jiang M, Shang W (2010) DTHMM based delay modeling and prediction for networked control systems. J Syst Eng Electron 21(6):1014–1024
Srinivasan S, Vallabhan M, Ramaswamy S, Kotta U (2013) Adaptive regulator for networked control systems: MATLAB and true time implementation. In: Chinese Control and decision conference (CCDC), 25th Chinese. IEEE, pp 2551–2555
Seshadhri S, Subathra B (2015) A comparitive analysis of neuro fuzzy and recurrent neuro fuzzy model based controllers for real-time industrial process. Syst Sci Control Eng 3:412–426
Zeng J, Qiao W (2011) Short-term solar power prediction using an RBF neural network. In: Power and energy society general meeting. IEEE, pp 1–8
Leung MT, Chen AS, Daouk H (2000) Forecasting exchange rates using general regression neural networks. Comput Oper Res 27(11):1093–1110
Ben-Nakhi AE, Mahmoud MA (2004) Cooling load prediction for buildings using general regression neural networks. Energy Convers Manag 45(13):2127–2141
Kayaer K, Yıldırım T (2003) Medical diagnosis on Pima Indian diabetes using general regression neural networks. In: Proceedings of the international conference on artificial neural networks and neural information processing (ICANN/ICONIP), pp 181–184
Specht DF (1991) A general regression neural network. Neural Networks IEEE Trans 2(6):568–576
Nadaraya EA (1964) On estimating regression. Theor Probab Appl 9(1):141–142
Vallabhan M, Seshadhri S, Ashok S, Ramaswmay S, Ayyagari R (2012) An analytical framework for analysis and design of networked control systems with random delays and packet losses. In: Proceedings of the 25th Canadian conference on electrical and computer engineering (CCECE)
Srinivasan S, Buonopane F Subathra B, Ramaswamy S (2015) Modelling of random delays in networked automation systems with heterogeneous networks using data-mining techniques. In: Proceedings of the 2015 conference on automation science and engineering (CASE 2015), Gothernberg, Sweden, pp. 362–368
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Sreram, B., Bounapane, F., Subathra, B., Srinivasan, S. (2016). Estimating Random Delays in Modbus Over TCP/IP Network Using Experiments and General Linear Regression Neural Networks with Genetic Algorithm Smoothing. In: Suresh, L., Panigrahi, B. (eds) Proceedings of the International Conference on Soft Computing Systems. Advances in Intelligent Systems and Computing, vol 398. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2674-1_58
Download citation
DOI: https://doi.org/10.1007/978-81-322-2674-1_58
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2672-7
Online ISBN: 978-81-322-2674-1
eBook Packages: EngineeringEngineering (R0)