Abstract
This chapter shows models for time delays and others network imperfections generated into NCS and how they are integrated into control, scheduling or codesign algorithms. First, a time delay model is presented using a generalized exponential distribution based function with data collect from non-deterministic networks. After, three NCS models are presented, each incorporates information about the network imperfections with the ultimate aim of generating a corrective action. We present models based on control, communication and codesign methodologies. Finally, a neuro-fuzzy identification is presented to model the system states and estimate the parameters of the NCS based on multi-sampling periods.
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References
Carnevale, D., Teel, A.R., Nesic, D.: A Lyapunov proof of improved maximum allowable transfer interval for networked control systems. IEEE Trans. Autom. Control 55, 892–897 (2007)
Mendez-Monroy, E.: Codiseño de Sistemas de Control en Red Compensando Imperfecciones Acotadas de Tiempo Inducidos por la Red. Posgrado en Ingenieria, Campo de Conocimiento de Electrica, PhD, UNAM. 22 Junio (2012)
Ma, C., Chen, S., Liu, W.: Maximum allowable delay bound of networked control systems with multi-step delay. Simul. Model. Pract. Theor. 15(5), 513–520 (2007)
Lian, F., Moyne, J., Otanez, P., Tilbury, D., Moyne, J.: Design of sampling and transmission rates for achieving control and communication performance in networked multi-agent system. In: Proceedings of American Control Conference, pp. 3329–3334 Denver, USA (2003)
Gupta, V., Spanos, D., Hassibi, B., Murria, M.R.: On LQG control across a stochastic packet-dropping link. In: Proceedings 2005 American Control Conference, pp. 360–365, Portland, USA (2005)
Cloosterman, M., van de Wouw, N., Heemels, W., Nijmeijer, H.: Stabilization of networked control systems with large delays and packet dropouts. In: American Control Conference, pp. 4991–4996 (2008)
Meng, X., Lam, J., Gao, H.: Network-based H\(\infty \) control for stochastic systems. Int. J. Robust Nonlinear Control 19(3), 295–312 (2009)
Jiang, X., Han, Q.L., Liu, S., Xue, A.: A new H\(\infty \) stabilization criterion for networked control systems. IEEE Trans. Autom. Control 53(4), 1025–1032 (2008)
Millan P., Orihuela L., Vivas c., Rubio F.R.: Control Optimo-L2 Basado en Red Mediante Funcionales de Lyapunov-Krasovskii, Revista Iberoamericana de Automatica e Informatica Industrial RIAI 9(1), pp. 14–23 (2012)
Ogata, K.: Discrete-Time Control Systems. Prentice-Hall Inc., Upper Saddle River, NJ, USA (1987)
Esquivel-Flores, O.A.: Estudio de Sistemas Multi-agentes Reconfigurables. Posgrado en Ciencias e Ingenieria de la Computacion, UNAM. 23 Enero (2013)
Aström, K.J., Wittenmark, B.: Computer-Controlled Systems: Theory and Design, 2nd edn. Prentice-Hall Inc., Englewood Cliffs, NJ (1990)
Nilsson, J.: Real-Time control systems with delays; PhD Thesis, Dept. Automatic Control. Lund Institute of Technology, Lund, Sweden (1998)
Marti, P., Velasco, M.: Toward flexible scheduling of real-time control tasks: reviewing basic control models. In: Proceedings of the 10th International Conference on Hybrid Systems, Computation and Control. LNCS (2007)
Tipsuwan, Y., Chow, M.Y.: On the gain scheduling for networked pi controller over IP network. IEEE/ASME Trans. Mech., 9–3 (2004)
Tanaka, K., Wang, H. O.: Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach. Wiley (2001)
Mendez-Monroy, P.E., Benitez-Perez, H.: Fuzzy control with estimated variable sampling period for non-linear networked control systems: 2-DOF helicopter as case study. Trans. Inst. Meas. Control 34(7), 802–814 (2012)
Mendez-Monroy, P.E., Benitez-Perez, H.: Identification and control for discrete dynamics systems using space state recurrent fuzzy neural networks. In: Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007), pp. 112–117. IEEE (2007)
Gonzalez-Olvera, M.A., Tang, Y.: A new recurrent neurofuzzy network for identification of dynamic systems. Fuzzy Sets Syst. 158(10), 1023–1035 (2007)
Benitez-Perez, H., Benitez-Perez, A., Ortega-Arjona, J.: Networked control systems design considering scheduling restrictions and local faults using local state estimation. IJICIC 9–8, 3225–3239 (2013)
Cervin, A., Henriksson, D., Lincoln, B., Eker, J., Arzen, K.-E.: How does control timing affect performance? In: Analysis and Simulation of Timing using Jitterbug and Truetime. Control Systems, IEEE 23(3), 16–30 (2003)
We, S., Er, M.J., Gao, Y.: A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans. Fuzzy Syst. 4(4), 578–598 (2001)
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Benítez-Pérez, H., Ortega-Arjona, J.L., Méndez-Monroy, P.E., Rubio-Acosta, E., Esquivel-Flores, O.A. (2019). Modelling of Networked Control Systems. In: Control Strategies and Co-Design of Networked Control Systems . Modeling and Optimization in Science and Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-97044-8_2
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DOI: https://doi.org/10.1007/978-3-319-97044-8_2
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