Direct Adaptive Control Based on Gradient Estimation

›› 2008, Vol. 16 ›› Issue (5): 752-761.

• SYSTEM ENGINEERING • Previous Articles Next Articles

Direct Adaptive Control Based on Gradient Estimation

ZHAO Zhong¹, MA Nannan¹, PAN Lideng¹, XU Ning², SUN Kang^1,2

1. Research Institute of Automation, Beijing University of Chemical Technology, Beijing 100029, China;
2. Beijing Research Institute of Chemical Technology, SINOPEC Corp., Beijing 100023, China

Received:2007-10-01 Revised:2008-01-21 Online:2008-10-28 Published:2008-10-28
Supported by:
the National Natural Science Foundation of China(60774080);BJNOVA 2005B15.

Direct Adaptive Control Based on Gradient Estimation

赵众¹, 马楠楠¹, 潘立登¹, 徐宁², 孙康^1,2

1. Research Institute of Automation, Beijing University of Chemical Technology, Beijing 100029, China;
2. Beijing Research Institute of Chemical Technology, SINOPEC Corp., Beijing 100023, China

通讯作者: ZHAO Zhong,E-mail:zhaozhong@mail.buct.edu.cn
基金资助:
the National Natural Science Foundation of China(60774080);BJNOVA 2005B15.

Abstract

Abstract: A control method of direct adaptive control based on gradient estimation is proposed in this article.The dynamic system is embedded in a linear model set.Based on the embedding property of the dynamic system,an adaptive optimal control algorithm is proposed.The robust convergence of the proposed control algorithm has been proved and the static control error with the proposed method is also analyzed.The application results of the proposed method to the industrial polypropylene process have verified its feasibility and effectiveness.

Key words: direct adaptive control, embedding property, gradient estimation, robust convergence, static error, polypropylene process

摘要： A control method of direct adaptive control based on gradient estimation is proposed in this article.The dynamic system is embedded in a linear model set.Based on the embedding property of the dynamic system,an adaptive optimal control algorithm is proposed.The robust convergence of the proposed control algorithm has been proved and the static control error with the proposed method is also analyzed.The application results of the proposed method to the industrial polypropylene process have verified its feasibility and effectiveness.

关键词: direct adaptive control, embedding property, gradient estimation, robust convergence, static error, polypropylene process

ZHAO Zhong, MA Nannan, PAN Lideng, XU Ning, SUN Kang. Direct Adaptive Control Based on Gradient Estimation[J]. , 2008, 16(5): 752-761.

赵众, 马楠楠, 潘立登, 徐宁, 孙康. Direct Adaptive Control Based on Gradient Estimation[J]. , 2008, 16(5): 752-761.

References

1 Qin, J.S., Badgwell, T.A., “A survey of industrial model predictive control technology”, Control Engineering Practice, 11 (5), 733-764 (2003).
2 Marsik, J., Strejic, V., “Application of identification-free algorithm for adaptive control”, Automatica, 25 (2), 273-277 (1989).
3 Astrom, K.J., Hang, C.C., Persson, P., Ho, W.H., “Towards intelligent PID control”, Automatica, 28 (1), 1-9 (1992).
4 Levin, A.U., Narendra, K.S., “Control of nonlinear dynamical systems using neural networks:controllability and stabilization”, IEEE Trans. NN, 4, 192-206 (1993).
5 Levin, A.U., Narendra, K.S., “Control of nonlinear dynamical systems using neural networks-Part (II) Observability , identification and control”, IEEE Trans. NN, 7, 30-42 (1996).
6 Narendra, K.S., Parthasarathy, K., “Identification and control of dynamic system using neural networks”, IEEE Trans. NN, 1 (1), 4-27 (1990).
7 Lewis, F.L., Yesildirek, A., Liu, K., “Multilayer neuralnet robot controller with guaranteed tracking performance”, IEEE Tran. NN, 7 (2), 388-398 (1996).
8 Polycarpou, M.M., “Stable adaptive neural control scheme for nonlinear systems”, IEEE Trans. AC, 41 (3), 447-451 (1996).
9 Yesidirek, A., Lewis, F.L., “Feedback linearization using neural networks”, Automatica, 31 (11), 1659-1664 (1995).
10 Ge, S.S., Lee, T.H., Li, G.Y., Zhang, J., “Adaptive NN control for a class of discrete-time nonlinear systems”, International Journal of Control, 76 (4), 334-354 (2003).
11 Ge, S.S., Lee, T.H., Li, G.Y., “Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems”, Automatica, 39 (3), 807-819 (2003).
12 Spall, J.C., Cristion, J.A., “A neural network controller for systems with unmodeled dynamics with applications to wastewater treatment”, IEEE Trans SMC-B:Cybernetics, 27 (3), 369-375 (1997).
13 Spall, J.C., Cristion, J.A., “A model free control of nonlinear stochastic systems with discrete-time measurement”, IEEE Trans AC, 43 (9), 1198-1210 (1998).
14 Rough, W.J., Shamma, J.S., “Research on gain scheduling”, Automatica, 36 (6), 1401-1425 (2000).
15 Rardin, R.L., Optimization in Operations Research, Prentice Hall, New York, 100-166 (1998).
16 Fang, C., Xiao, D., Process Identification, Tsinghua University Press, Beijing, 210-275 (1988).
17 Chui, C.K., An Introduction to Wavelet, Academic Press, New York, USA, 170-215 (1990).
18 Mallat, S., “A theory for multiresolution signal decomposition:the wavelet representation”, IEEE Trans. Pat. Anal. Mach. Intel., 11, 674-691 (1989).
19 Zhao, Z., Jin, Y.H., Wang, J.C., “Application of wavelet transform to process operating region recognition”, Journal of Chemical Engineering of Japan, 33 (6), 823-831 (2000).
20 Honeywell, Inc., RPID User's Guide, Houston, USA (2000).
21 Honeywell, Inc., Profit Loop User's Guide, Houston, USA (2000).

Direct Adaptive Control Based on Gradient Estimation

Direct Adaptive Control Based on Gradient Estimation

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 0

Recommended Articles

Metrics

Comments