Comparison between GPC and adaptive GPC based on Takagi Sugeno multi-model for an Activated Sludge Reactor

• Autorzy: Matoug L. , Khadir M. T.
• Języki publikacji: EN

Abstrakty

EN

This paper investigates the use of Adaptive Generalized Predictive Control (TS-AGPC) for an activated sludge reactor, based on a Takagi Sugeno (TS) model, and presents the comparison between the latter and Generalized Predictive Control using an overall TS model (TS-GPC). The reduced bio-reactor Activated Sludge ASM1 Model, which describes the biological degradation of an activated sludge reactor, is designed based on several simplifications, as a TS model, its structure being based on a set of linear submodels, covering the process input-output space, interpolated by a nonlinear weighting function µ. The adaptive GPC approach is obtained by switching between linear submodels of the TS formulation. This is performed by selecting, in turns, a portion of the weighting function µ. The winning model will then act as an internal model for the TS-AGPC control law formulation, whereas the complete TS model is used in the calculation of the TS-GPC control law. Finally, the performance under input and parametric disturbances as well as control variable constraints of the TS-AGPC controller are compared to those for a global TS-GPC controller and a benchmark PID in terms of error and response dynamics.

Słowa kluczowe

EN

adaptive generalized predictive control; generalized predictive control; Takagi Sugeno; activated sludge reactor; activated sludge model

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2017
• Tom: Vol. 46, no. 2
• Strony: 147--176
• Opis fizyczny: Bibliogr. 33 poz., rys.

Twórcy

autor

Matoug L.

• Badji-Mokhtar Annaba University, LabGED Laboratory, BP 12, 23200 Annaba, Algeria

autor

Khadir M. T.

• Badji-Mokhtar Annaba University, LabGED Laboratory, BP 12, 23200 Annaba, Algeria

Bibliografia

• [1] Caraman, S., Sbarciog, M. and Barbu, M. (2007) Predictive Control of a Wastewater Treatment Process. International Journal of Computers, Communications and Control, 2, 132-142.
• [2] Clarke, D. W., Mohtadi, C. and Tuffs, P. S. (1987) Generalised predictive control - part I. The basic algorithm. Automatica, 23(2), 137-148.
• [3] Cutler, C. R. and Ramaker, B. L. (1979) Dynamic Matrix Control - A Computer Control Algorithm. Proc. of the 86th National Meeting of the American Institute of Chemical Engineers, No. 51-B, Houston, TX.
• [4] Cutler, C. R. (1983) Dynamic matrix control: an optimal multivariable control algorithm with constraints. PhD dissertation, University of Houston TX.
• [5] Escano, J. M., Bordons, C., Vilas, C., Garcia, M. R. and Alonso, A. A. (2009) Neurofuzzy model based predictive control for thermal batch processes. Journal of Process Control, 19, 1566-1575.
• [6] Froisy, J. B. (1994) Model predictive control: Past, present and future. ISA Transactions, 33, 235-243.
• [7] Garcia, C. E., Prett, D. M., Morari, M. and Papon, J. (1989) Model predictive control: theory and practice; a survey. Automatica, 25(3), 335-348.
• [8] Garcia, C. E. and Morshedi, A. M. (1986) Quadratic Programming Solution of Dynamic Matrix Control (QDMC). Chemical Engineering Communications, 46, 073-087.
• [9] Gasso, K. (2000) Identification des syst`emes dynamiques non-lin´eaires: approche multi-mod`eles. PhD dissertation, INPL.
• [10] Guerra, T., Kruszewski, A. and Bernal, M. (2009) Control law proposition for the stabilization of discrete Takagi-Sugeno models. IEEE Trans. on Fuzzy Systems, 17, 724-731.
• [11] Henze, M., Grady Jr., C. P. L., Gujer, W., Marais, G. R. and Matsuo, T. (1987) Activated sludge model no. 1. Scientific and Technical Report No.1. IAWPRC, London, 33.
• [12] Huang, Y. and Jadbabaie, A. (1999) Nonlinear Hinf Control: An enhanced Quasi-LPV Approach. Proc. of the IFAC World Congres, 85-90.
• [13] Jeppsson, U. (1996) Modelling aspects of wastewater treatment processes. PhD dissertation, LTH-IEA-1010.
• [14] Kalman, R. E. (1960a) Contributions to the theory of optimal control. Automatica, 5, 102-119.
• [15] Kalman, R. E. (1960b) A new approach to linear filtering and prediction problems. Transactions of ASME, Journal of Basic Engineering, 87, 35-45.
• [16] Li, N., Li, S. Y. and Xi, Y. G. (2004) Multi-model predictive control based on the Takagi Sugeno fuzzy models - a case study. Information Sciences, 165, 247-263.
• [17] Matoug, L. and Khadir, M. T. (2012) Mod`ele floue Takagi Sugeno d’une station d’´epuration `a boues activ´ees. International Conference on Embedded Systems in Telecommunications and Instrumentation (ICESTI’12). No number 2012, Annaba, Algeria.
• [18] Matoug, L. and Khadir M. T. (2014) Multi-Model Predictive Control Strategies for an Activated Sludge Model. 2014 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE Publi cations, 504–509.
• [19] Matoug, L. and Khadir M. T. (2015) Dynamic Model Prediction Control for an Activated Sludge Model based on a T-S Multi-Model. 2015 3rd Conference on Control, Engineering & Information Technology (CEIT). IEEE Publications, 1–6.
• [20] Mor`ere, Y. (2001) Mise en oeuvre de lois de commande pour les mod`eles flous de type Takagi-Sugeno. PhD dissertation, Universit´e de Valenciennes et du Hainaut-Cambr´esis.
• [21] Murray-Smith, R. and Johansen, T. (1997) Multiple Model Approaches to Modeling and Control. Taylor and Francis, London.
• [22] Nagy, A. M., Mourot, G., Marx, B., Schutz, G. and Ragot, J. (2010) Systematic Multimodeling Methodology Applied to an Activated Sludge Reactor Model. Industrial and Engineering Chemistry Research, 49(6), 27902799.
• [23] Nagy, A. M. (2010) Analyse et synth`ese de multimod`eles pour le diagnostic. Application `a une station d’´epuration. PhD dissertation, INPL, Nancy.
• [24] Prett, D. M. and Gillette, R. D. (1980) Optimization and Constrained Multivariable Control of a Catalytic Cracking unit. In: Proceedings of the joint automatic control conference, Paper WP5-C IEEE Publications.
• [25] Qin, S. J. and Badgwell, T. A. (1996) An overview of industrial model predictive control technology. In: Chemical process control-CPC V. https://www.researchgate.net/publication2773527.
• [26] Qin, S. J. and Badgwell, T. A. (2003) A survey of industrial model predictive control technology. Control Engineering Practice, 11, 733-764.
• [27] Richalet, J., Rault, A., Testud, J. L. and Papon, J. (1976) Algorithmic control of industrial processes. In: Proceedings of the 4th IFAC symposium on identification and system parameter estimation, 87, 1119-1167.
• [28] Richalet, J., Rault, A., Testud, J. L. and Papon. (1978) Model predictive heuristic control: Applications to industrial processes. Automatica, 14, 413-428.
• [29] Richalet, J. (1993) Pratique de la commande predictive. Hermes ed., Trait´e des Nouvelles Technologies, Serie Automatique.
• [30] Smets, I., Verdickt, L. and Van Impe, J. (2006) A linear ASM1 based multi- model for activated sludge systems. Mathematical and Computer Modeling of Dynamical Systems, 12, 489-503.
• [31] Takagi, T. and Sugeno, M. (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Trans. Syst., Man and Cybernet., 15, 116-132.
• [32] Tanaka, K. and Wang, H.O. (2001) Fuzzy Control Systems Design and Analysis: a Linear Matrix Inequality Approach. John Wiley and Son Eds., New York.
• [33] Wang, H. O., Tanaka, K. and Griffin, M. (1996) An approach to fuzzy control of nonlinear systems: stability and design issues. IEEE Trans. on Fuzzy Systems, 4, 14-23.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).