Model Predictive Control Based on Modified Smith Predictor for Networked Control Systems

Document Type : Original Article

Authors

1 Dept. of Control Eng., High Institute of Engineering, Belbies, Egypt

2 Dept. of Industrial Electronics and Control Eng., Faculty of Elect., Eng., Menoufia University.

Abstract

This study presents a predictive controller for networked control systems (NCSs), which is a model predictive control (MPC) combined with Smith predictor. The network delays and data dropouts are problems, which greatly weaken the controller performance. In the proposed controller, there are two internal loops. The first is the loop around the MPC with the model of the system, which predicted future outputs. The other is the loop around the plant to give the error between the model and actual plant. The proposed controller is designed for controlling a DC servo system via a network. The practical results based on Matlab/Simulink are established. The practical results show that the performance of the proposed controller is greatly improved over a wide range of networked time delay and data dropouts comparing to other controllers.

References

[1] T. Wang, H. Gao, J. Qiu, A combined adaptive neural network and nonlinear
model predictive control for multirate networked industrial process control. IEEE
Transactions on Neural Networks and Learning Systems, 27(2016) 416-425
[2] Z. Li, D. Sun, Y. Shi, L. Wang, A stabilizing model predictive control for
networked control system with data packet dropout. Journal of Control Theory and
Applications. 7(2009) 281-284
[3] P. M. Marusak, Advantages of an easy to design fuzzy predictive algorithm in
control systems of nonlinear chemical reactors. Applied Soft Computing. 9(2009)
1111-1125
[4] J. Qiu, H. Gao, S. X. Ding, Recent advances on fuzzy-model-based nonlinear
networked control systems: a survey. IEEE Transactions on Industrial
Electronics. 63(2016) 1207-1217
[5] T. Wang, H. Gao, J. Qiu, A combined fault-tolerant and predictive control for
network-based industrial processes. IEEE Transactions on Industrial
Electronics. 63(2016)2529-2536
[6] Y. Xia, W. Xie, B. Liu, X. Wang, Data-driven predictive control for networked
control systems. Information Sciences. 235(2013) 45-54
[7] J. Qiu, H. Gao, S. X. Ding, Recent advances on fuzzy-model-based nonlinear
networked control systems: a survey. IEEE Transactions on Industrial
Electronics. 63(2016) 1207-1217
[8] B. Xuhui, H. Zhongsheng, J. Shangtai, C. Ronghu, An iterative learning control
design approach for networked control systems with data dropouts. International
Journal of Robust and Nonlinear Control. 26(2016) 91-109
[9] C. Lazar, S. Carari, A remote-control engineering laboratory. IEEE Transactions
on Industrial Electronics. 55(2008) 2368-2375
[10] Y. Tipsuwan, M. Y. Chow, Gain scheduler middleware: a methodology to enable
existing controllers for networked control and teleoperation-part I: networked
control. IEEE Transactions on Industrial Electronics. 51(2004) 1218-1227
0px; "> [11] M. Y. Chow, Y. Tipsuwan, Gain adaptation of networked DC motor controllers
based on QoS variations. IEEE Transactions on Industrial Electronics. 50(2003)
936-943
[12] D. D. Blair, D. R. Jensen Doan, T. K. Kim, Networked intelligent motor-control
systems. IEEE Industry Applications Magazine. 7(2001) 18-25
[13] L. Samaranayake, S. Alahakoon, K. Walgama, Speed controller strategies for
distributed motion control via Ethernet. In IEEE International Symposium on
Intelligent Control. (2003) 322-327
[14] P. Seiler, R. Sengupta, An H-infinity approach to networked control, IEEE
Transactions on Automatic Control. 50(2005) 356–364
[15] M. M. B. Gaid, A. Cela, Y. Hamam, Optimal integrated control and scheduling of
networked control systems with communication constraints: application to a car
suspension system. IEEE Transactions on Control Systems Technology. 14(2006)
776-787
[16] R. A. Gupta, M. Y. Chow, Networked control system: overview and research
trends. IEEE Transactions on Industrial Electronics. 57(2010) 2527-2535
[17] M. Vallabhan, S. Seshadhri, S. Ashok, S. Ramaswmay, R. Ayyagari, An analytical
framework for analysis and design of networked control systems with random
delays and packet losses. arXiv preprint arXiv: (2015) 1506.06261
[18] H. S. Park, Y. H. Kim, D. S. Kim, W. H. Kwon, A scheduling method for
network-based control systems. IEEE Transactions on Control Systems
Technology. 10(2002) 318-330
[19] P. V. Zhivoglyadov, R. H. Middleton, Networked control design for linear
systems. Automatica. 39(2003) 743-750
[20] G. P. Liu, Y. Xia, J. Chen, D. Rees, W. Hu, Networked predictive control of
systems with random network delays in both forward and feedback channels. IEEE
Transactions on Industrial Electronics. 54(2007) 1282-1297
[21] H. Zhang, Y. Shi, A. S. Mehr, Robust H∞ PID control for multivariable networked
control systems with disturbance/noise attenuation. International Journal of Robust
and Nonlinear Control. 22(2012)183-204
[22] G. P. Liu, Y. Xia, J. Chen, D. Rees, W. Hu, Networked predictive control of
systems with random network delays in both forward and feedback channels. IEEE
Transactions on Industrial Electronics. 54(2007) 1282-1297
[23] D. Srinivasagupta, H. Schättler, J B. oseph, Time-stamped model predictive
control: an algorithm for control of processes with random delays.Computers and
Chemical Engineering. 28(2004) 1337-1346
[24] Q. Zhang, C. Lin, P. Chen, Time-stamped dynamic matrix control for networked
control systems. In 2005 IEEE International Conference on Industrial Technology.
(2005) 1159-1163
[25] G. P. Liu, J. Mu, D. Rees, Networked predictive control of systems with random
communication delay. In UKACC Intl. Conf. on Control. (2004).
[26] J. Mu, G. P. Liu, D. Rees, Design of robust networked predictive control systems.
In Proceedings of the 2005, American Control Conference. (2005) 638-643
[27] P. Tang, C. W. de Silva, Stability and optimality of constrained model predictive
control with future input buffering in networked control systems. American
Control Conference. New York: IEEE Press. (2005) 1245 – 1250
"> [28] P. Varutti, B. Kern, T. Faulwasser, R. Findeisen, Event-based model predictive
control for networked control systems. Proceedings of the 48th IEEE Conference
on Decision and Control, (2009) 567-572
[29] E. Perea-Lopez, B. E. Ydstie, I. E. Grossmann, A model predictive control strategy
for supply chain optimization. Computers & Chemical Engineering. 27(2003)
1201-1218
[30] H. M. Khalil, M. El Bardini, Support vector machines based adaptive controller
for piston hydraulic motor. algorithms, 4 (2011).
[31] E. ZAFIRIOU, M. MORARI, Design of robust digital controllers and samplingtime selection for SISO systems. International Journal of Control, 44(1986), 711-
735.
[32] R. Scattolini, S. Bittanti, On the choice of the horizon in long-range predictive
control—some simple criteria. Automatica, 26(1990), 915-917.
[33] B.W. Hogg, N. M. El-Rabaie, Generalized predictive control of steam pressure in
a drum boiler. IEEE Transactions on Energy Conversion. 5(1990) 485-492
[34] D. W. Clarke, C. Mohtadit, P. S. Tuffs, Generalized predictive vontrol Part II.
Extensions and Interpretations. Automatica. 23(1987) 149-160
[35] W. Du, F. Du, Novel smith predictor and fuzzy control for networked control
systems. In Information Processing, 2009. APCIP 2009. Asia-Pacific Conference
on, 1(2009) 75-78
[36] A. M. El-Nagar, Embedded intelligent adaptive PI controller for an
electromechanical system. ISA transactions, (In Press) (2016).
[37] H. Zhang, Z. Li, Simulation of networked control system based on Smith
compensator and single neuron Incomplete differential forward PID. JNW, 6
(2011), 1675-1681.
[38] A. Sakr, A. M. El-Nagar, M. El-Bardini, M. Sharaf, Fuzzy smith predictor for
networked control systems. In Computer Engineering & Systems (ICCES), 2016
11th International Conference on (pp. 437-443). IEEE.
 

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