Abstract
A model dedicated to Chinese medicine sugar precipitation was designed, without consideration of crystal size distribution. Sliding mode adaptive control algorithm was proposed for the uncertain nonlinear systems based on Lyapunov’s stability theory. The system was divided into nominal model and lumped disturbance term which embodies model mismatch, parameter uncertainties, and disturbances. Adaptive control was adopted to approach the uncertain input coefficients of system, robust control was introduced to reduce the lumped disturbance to a small bound in finite time, and sliding mode control was adopted to eliminate the tracking errors of the uncertain nonlinear system ultimately. The scheme is robust for the uncertainties and overcomes the chattering in the input of sliding mode control. It was applied to the precipitation control of sucrose-glucose mixed solution, and the validity of the proposed algorithm was supported by simulation results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Paengiuntuek, W., Kittisupakon, P., Arpornwichanop, A.: Optimization and Nonlinear Control of a Batch Crystallization Process. Journal of the Chinese Institute of Chemical Engineers 39, 249–256 (2008)
Nagy, Z.K., Chew, J.W., Fujiwara, M.: Comparative Performance of Concentration and Temperature Controlled Batch Crystallizations. Journal of Process Control 18, 399–407 (2008)
Georgieva, P., Azevedo, S.F.D.: Neural Network-based Control Strategies Applied to a Fed-batch Crystallization Process. International Journal of Information and Mathematical Sciences 33, 224–233 (2007)
Nagy, Z.K.: Model Based Robust Control Approach for Batch Crystallization Product Design. Computers and Chemical Engineering 33, 1685–1691 (2009)
Bonvin, D.: Optimal Operation of Batch Reactors: A Personal View. Journal of Process Control 8, 355–368 (1998)
Chen, Z.G., Xu, C., Shao, H.H.: Batch Processes Optimization and Advanced Control—A Survey. Control and Instruments in Chemical Industry 30, 1–6 (2003)
Xiong, Z.H., Zhang, J.: Neural Network Model-based On-line Re-optimization Control of Fed-batch Processes Using a Modified Iterative Dynamic Programming Algorithm. Chemical Engineering and Processing 44, 477–484 (2005)
Xiong, Z.H., Zhang, J., Dong, J.: Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model. Chinese Journal of Chemical Engineering 16, 235–240 (2008)
Zhang, J., Nguyen, J., Xiong, Z.H.: Iterative Learning Control of Batch Processes Based on Time Varying Perturbation Models. Journal of Tsinghua University (Sci & Tech) 48(S2), 1771–1774 (2008)
Jia, L., Shi, J.P., Qiu, M.S.: Nonrestraint-iterative Learning-based Optimal Control for Batch Processes. CIESC Journal 61, 1889–1893 (2010)
Damour, C., Benne, M., Boillereaux, L.: NMPC of an Industrial Crystallization Process Using Model-based Observers. Journal of Industrial and Engineering Chemistry 16, 708–716 (2010)
Fan, L., Wang, H.Q., Song, Z.H.: Iterative Optimal Control for Batch Process Based on Generalized Predictive Control. Control and Instruments in Chemical Industry 33, 25–28 (2006)
Liu, Z.L., Svoboda, J.: A New Control Scheme for Nonlinear Systems with Disturbances. IEEE Transactions on Control Systems Technology 14, 176–181 (2006)
Xu, H.J., Maj, M.: Robust Adaptive Sliding Control for a Class of MIMO Nonlinear Systems. In: AIAA Guidance, Navigation and Control Conference and Exhibit, Montreal, Canada, pp. 1–10 (2001)
Pai, M.C.: Design of Adaptive Sliding Mode Controller for Robust Tracking and Model Following. Journal of Franklin Institute 347, 1837–1849 (2010)
Ansarifar, G.R., Talebi, H.A., Davilu, H.: An Adaptive-dynamic Sliding Mode Controller for Non-minimum Phase Systems. Commun Nonlinear Sci. Numer Simulat. 17, 414–425 (2012)
Damour, C., Benne, M., Boillereaux, L.: Multivariable Linearizing Control of An Industrial Sugar Crystallization Process. Journal of Process Control 21, 46–54 (2011)
Wright, P.G., White, E.T.: A Mathematical Model of Vacuum Pan Crystallization. In: Proceedings of ISSCT 15th Congress, pp. 1546–1560 (1974)
Barth, S.: Utilization of FBRM in the Control of CSD in a Batch Cooled Crystallizer. Ph.D. Thesis, Georgia Institute of Technology (2006)
Damour, C., Benne, M., Boillereaux, L.: Multivariable Linearizing Control of an Industrial Sugar Crystallization Process. Journal of Process Control 21, 46–54 (2011)
Larsen, P.A., Patience, D.B., Rawlings, J.B.: Industrial Crystallization Process Control. IEEE Control System Magazine, 70–80 (2006)
Mesbah, A., Landlust, J., Huesman, A.E.M.: A Model-based Control Framework for Industrial Batch Crystallization Processes. Chemical Engineering Research and Design 88, 1223–1233 (2010)
Damour, C., Benne, M., Grondin-Perez, B.: Nonlinear Predictive Control Based on Artificial Neural Network Model for Industrial Crystallization. Journal of Food Engineering 99, 225–231 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Q., Duan, H. (2013). Dynamic Model and Sliding Adaptive Control of a Chinese Medicine Sugar Precipitation Process. In: Liu, D., Alippi, C., Zhao, D., Hussain, A. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2013. Lecture Notes in Computer Science(), vol 7888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38786-9_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-38786-9_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38785-2
Online ISBN: 978-3-642-38786-9
eBook Packages: Computer ScienceComputer Science (R0)