Adaptive Predictive Feedback Techniques for Network Control system

International Research Journal of Electronics & Communication Engineering (IRJECE) ISSN: Applied Volume 1 Issue 1 (December 2014) www.irjece.com

Adaptive Predictive Feedback Techniques for Network Control system Mr.Sunil kumar1

Mr. Kapil Dev Sharma

Department of Electricals and Electronics Dronacharya Group of Institution Gr. Noida

Department of Electricals and Electronics Dronacharya Group of Institution Gr. Noida

Abstract - In this Research work, adaptive predictive feedback control is used to suppress plate vibrations. The adaptive predictive controller consists of an on-line identification technique coupled with a control scheme. Networked control systems (NCS) are distributed computing and control systems with sensors, actuators and controllers that communicate over a shared medium. The distributed nature of NCS and issues related to the shared communication medium pose significant challenges for control design, as the control system no longer follows the rules of classical control theory. The main problems that are not well covered by the traditional control theory are varying time delays and packet losses. The change of communication architecture introduces different forms of time delay uncertainty in the closed loop system dynamics, It is also shown that the adaptive controller has the ability to track changes in the disturbance spectrum as well as track a time varying plant under certain conditions. To eliminate the time delays pade’s approximation and smith predictor are implemented in MATLAB Simulink Keyword- Second order System, NCS 1.INTRODUCTION 1.1NETWORKED CONTROL SYSTEM The feedback control systems [1], where the process sensors, actuators, and controllers are interconnected by a communication networks are called Networked Control Systems (NCSs). It is a type of distributed control systems. There are the advantages of using the network in terms of reliability, reduced wiring, reconfigurability and ease of system diagnosis as all the information is available everywhere in the system. However implementing the communication network induces the stochastic and time varying delay which can degrade the performance of the system and even could make the system unstable. Moreover the time delays are the function of device processing times and communication rate Research in NCSs is different from that in conventional time-delay systems where time delays are assumed to be constant or bounded. Because of the variability of network-induced time delays, the NCSs may be time-varying systems which make analysis and design more difficult. 1.2 Adaptive Predictive Control Adaptive predictive control theory may be used to regulate a time varying plant in the presence of disturbances with varying characteristics. This chapter presents the theory of adaptive Multi-Input Multi-Output (MIMO) predictive controllers. Three control schemes are developed. The first updates the system identification (system ID) and controller parameters every time step2. The second updates the system ID every time step,but only updates the controller parameters periodically i.e., controller parameters are updated after a number of time steps. This is known as multirate adaptive control3. The third control scheme uses the identification technique to directly determine the controller parameters without the need of explicit system identification The combination of a system identification technique plus a control algorithm produces an adaptive controller. Traditionally, the SI is performed every time step with the controller being updated every time step as in Ref. [23]. A review of self-tuning predictive controllers which adapt every time step for nonminimum-phase system may be found in Ref. [25], while Ref. [26] uses the LQR solution as an adaptive controller.

Fig 1 Block diagram of plant with NCS 2.Modeling of Second order processes using Two Tank Consider a two tank for as plant, +

+

=

( )

If A0 ≠0 then this equation yields

+2

+

=

( )

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International Research Journal of Electronics & Communication Engineering (IRJECE) ISSN: Applied Volume 1 Issue 1 (December 2014) www.irjece.com =

where

ξ=

,2

and

=

.

Equation (2) is in the standard form of a second order system.

Where

= is the natural period of oscillation of the system ξ = is damping factor = is steady-state or static or simply gain of the system ( )= ,

+

=

( ) = ( ) +2 +1 ( )......................first capacity

,,

+ = ( ).................second capacity In other world ,the first system affect the second by its output, but converse is not true .the corresponding transfer function ( ) ( ) ( )= are = , ( ) = ( ) = ,, ( ) The overall transfer function between the external input F1(t) and y2(t) is ( ) ( ) ( )= ∗ = ( )∗ ( )= , ∗ ( ) ( ) + 1 ,, + 1 ( ) G( ) = = ( )

Fig 2 second order cascade water tank where

=

,

∗

,,

, 2

=

,

+

,,



=

∗

G(s) = Reference model ,

+ ,,

+

= =

( ). ( ).

control law, u(t) = r0(t)w(t) + g0(t)y(t) adaptive laws

( )

= ϒ (t) w(t)

( )

= ϒ (t) y(t) Error e(t) = y(t) - ym(t) lim e(t) = 0 t-∞ where w is the set point, ym output of reference model, у output of plant, g0, r0 feedback and feedforward controller parameters, ϒ1, ϒ2 adaptive gains, e adaptive error. 3.Simulink

Fig.3Block diagram of Adaptive control ____________________________________________________________________________________________________ © 2014, IRJECE- All Rights Reserved Page -2

International Research Journal of Electronics & Communication Engineering (IRJECE) ISSN: Applied Volume 1 Issue 1 (December 2014) www.irjece.com

Fig.4Block diagram of Adaptive control with NCS 4.Result

Fig.5 Uncontrolled response of plant

Fig.6 no of packet time out at 1% plant data loss and 1% controller data loss

Fig.7 controlled process with NCS 20 % plant data losses and 20 % controller data losses with sampling period 0.01

Fig.8. 35% plant data losses and 5 % controller data losses with sampling period 0.01

Fig.9. 35 % plant data losses and 90 % controller data losses with sampling period 0.5 sec ____________________________________________________________________________________________________ © 2014, IRJECE- All Rights Reserved Page -3

International Research Journal of Electronics & Communication Engineering (IRJECE) ISSN: Applied Volume 1 Issue 1 (December 2014) www.irjece.com

Fig.10. 1 % plant data losses and 90 % controller data losses with sampling period 0.001 sec Conclusions and future scope From the results it is clear that when 35% plant data losses and 5% controller signal losses with sampling period of 0.001 sec of both, a delay of 1 second is produced in response .If plant data losses is 90% with a sampling period 0.5 second, then a delay of approximately 5.4 second is produced in response .If both plant and controller data losses are 90% with same sampling period of 0.5 second then delay is 5.4 second. It means controller signal is necessary for undelayed response with high sampling rate because in final result fig. no. 5 only 1 % plant and controller data losses with sampling period 0.001 second then there is no delay in response. So finally we can say that delay increases with data losses in the network and sampling period of plant. Applications of the networked control system improve the efficiency, the flexibility, and the reliability of large-scale systems in modern industries. At the same time, applications of NCS for remote control and monitoring reduce the time and cost of installation, reconfiguration, and maintenance. References [1] Ljung, L., System Identification, Theory for the User, Prentice Hall, 1987. [2]. Astrum, K.J., and Witenmark, B., Adaptive ontrol, Addison-Wesley Publishing Company, 1995. [3].Chalam, V.V., Adaptive Control Systems, Marcel Dekker, Inc., 1987. [4]. Goodwin, G.C. and Sin, K.S., Adaptive Filtering, Prediction, and Control, Prentice-Hall, 1984. [5]. Haykin, S., Adaptive Filter Theory, Prentice-Hall, 1996. [6]. Juang, J.-N., Applied System Identification, Prentice-Hall, 1994. [7]. Juang, J.-N., “Unification of Several System Realization Algorithms”, Journal of Guidance, Control, and Dynamics, Vol. 20, Num. 1, January- February, 1997, Pages 67-73. [8]. Juang, J.-N., “System Realization Using Information Matrix”, Journal of Guidance,Control, and Dynamics, Vol. 20, No.3, May-June 1997. [9]. Phan, M., Horta, L.G., Juange, J.-N., and Longman,R.W.,“ImprovementObserver/Kalman Filter Identification (OKID) by Residual Whitening,”Journal of Vibrations and Acoustics, Vol. 117, April 1995, pp. 232-239. [10]. Juang, J.-N., “State-Space System Realization With Input- and Output-Data Correlation”, NASA Technical Paper 3622, April 1997. [11]. Clarke, D.W., Mohtadi, C., and Tuffs, P.S.,“Generalized Predictive Control - Part I.The Basic Algorithm,” Automatica, Vol. 23, No. 2, pp. 137-148, 1987. [12]. Clarke, D.W., Mohtadi, C., and Tuffs, P.S.,“Generalized Predictive Control - Part II.Extensions and Interpretations,” Automatica, Vol. 23, No. 2, pp. 149-160, 1987. [13]. Kouvaritakis, B., Rossiter, J.A., and Chang, A.O.T., “Stable Generalized Predictive Control: an algorithm with guaranteed stability”, University of Oxford, Department of Engineering Science, Parks Road, Oxford. [14]. Meirovitch, Leonard, Dynamics and Control of Structures, John Wiley & Sons, 1990. [15]. Shoureshi, R., “Active Noise Control: A Marriage of Acoustics and Control”,Proceedings of the American Control Conference, Baltimore, Maryland, pp. 3444-2448,1994. [16]. Vipperman, J.S., Burdisso, R.A., and Fuller,C.R.,“Active Control of Broadband Structural Vibration Using the LMS Adaptive Algorithm”, Journal of Sound and Vibration, 166(2), pp. 283- 299, April 1992. [17]. Clarke, D.W., “Self-tuning Control of Nonminimum-phase Systems”, Automatica,Vol. 20, No. 5, pp. 501-517, 1984 [18]. Clarke, D.W., “Adaptive Generalized Predictive Control”, Department of Engineering Science, Parks Road, Oxford OX1 3PJ, UK [19]. Clarke, D. W., Mosca, E., and Scattolini, R.,Robustness of an Adaptive Controller”, IEEE ransactions on Automatic Control, Vol. 39, Num. 5, pp. 1052-1056, May 1994. [20]. Wang, W., Henriksen, H., “Direct Adaptive Generalized Predictive Control”,Proceedings of the American Control Conference, Chicago, U.S.A., pp. 2402-2406, 1992. [21]. Wang, W., “A Direct Adaptive Generalized Predictive Control Algorithm for MIMO Systems”, Int. J. Control, Vol. 60, No. 6, pp. 1371-1381, 1994. [22]. Juang, J.-N., and Phan, M., “Recursive Deadbeat Predictive Control”, 1997 American Control Conference, I97115A, Albuquerque Convention Center, New Mexico, June 4-6,1997. ____________________________________________________________________________________________________ © 2014, IRJECE- All Rights Reserved Page -4

International Research Journal of Electronics & Communication Engineering (IRJECE) ISSN: Applied Volume 1 Issue 1 (December 2014) www.irjece.com [23]. Eure, K.W. and Juang, J.-N., “Adaptive Predictive Feedback Control of Plate Vibrations”, To be released as NASA Technical Memorandum. [24]. Eure, K.W., and Juang, J.-N., "Broadband Noise Control Using Predictive Techniques," NASA Technical Memorandum 110320, January 1997 [25]. Eure, K.W., Juang, J.-N., and Silcox, R., “Block Adaptive Feedback Control of Plate Vibrations”, To be released as NASA Technical Memorandum. [26]. Ellite, S.J., Sutton, T.J.,Rafaely, B.,and Johnson, M., “Design of Feedback Controllers using a Feedforward Approach”, Active 95, Newport Beach, CA, USA, 1995. [27]. Fuller, C. R., Elliott, S. J., & Nelson, P. A., Active Control of Vibration, 1995. [28] T. Rahkonen, "Distributed industrial control systems - A critical review regarding openness," Control Engineering Practice, Vol. 3, No. 8, 1995, pp. 1155-1162. [29] Y. Tipsuwan and M. Y. Chow, "Control methodologies in networked control systems," Control Engineering Practice, Vol. 11, No. 10, 2003, pp. 1099-1111. [30] P. F. Hokayem and C. T. Abdallah, "Inherent issues in net-worked control systems: A survey," Proc. of American Control Conference, Boston, MA, Vol. 6, 2004, pp. 4897- 4902. [31] T. W. Jian, D. W. Novotny and N. L. Schmitz, “Characteristic Induction Motor Values for Variable Voltage Part Load Performance Optimization” IEEE Transactions on Power Apparatus and Systems, vol. PAS-102, pp. 38-46, 1983. [32] D. S. Kirschen, D. W. Novotny and T. A. Lipo, “Optimal Efficiency of an Induction Motor Drives,” IEEE Transactions on Energy Conversion, vol. EC-2, no. 1, pp. 70-75, 1987. [33] R. Krishnan and A. S. Bharadwaj, “ A Review of Parameter Sensitivity and Adaptation in Indirect Vector Controlled Induction Motor Drive Systems,”PESC’90 Record. 21st Annual IEEE Trans. on Power Electronics, vol. 6, no.4, pp. 695-703, Oct. 1991. [35] T. Iwasaki and T. Kataoka, “Application of an Extended Kalman Filter to Parameter Identification of an Induction Motor,” IEEE-IAS Annual Meeting Conference Record,pp. 248-253, 1989. [36] L. Loron and G. Laliberte, “Application of the Extended Kalman Filter to Parameter Estimation of Induction Motors,” The European Power

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