Abstract
The idea proposed in the paper consists in significant simplification of the control structure with a predictive control algorithm and a steady–state target optimization. It is done by application of only one fuzzy (nonlinear) dynamic control plant model for both: predictive control and set–point calculation. The approach exploits possibilities offered by a fuzzy model used by the predictive control algorithm. The fuzzy model is of Takagi–Sugeno type with step responses used as the local models. Such a model can be obtained relatively easy and well tuned using neural networks. The proposed approach, despite simplification of the control system, offers very good control performance. It is demonstrated using an example of a control system of a nonlinear chemical reactor with inverse response.
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References
Blevins, T.L., McMillan, G.K., Wojsznis, W.K., Brown, M.W.: Advanced Control Unleashed. ISA (2003)
Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, Heidelberg (1999)
Doyle, F., Ogunnaike, B.A., Pearson, R.K.: Nonlinear model–based control using second–order Volterra models. Automatica 31, 697–714 (1995)
Kassmann, D.E., Badgwell, T.A., Hawkins, R.B.: Robust Steady-State Target Calculation for Model Predictive Control. AIChE Journal 46, 1007–1024 (2000)
Lawrynczuk, M., Marusak, P., Tatjewski, P.: Cooperation of model predictive control with steady–state economic optimisation. Control and Cybernetics 37, 133–158 (2008)
Maciejowski, J.M.: Predictive control with constraints. Prentice Hall, Harlow (2002)
Marusak, P., Tatjewski, P.: Actuator fault tolerance in control systems with predictive constrained set-point optimizers. International Journal of Applied Mathematics and Computer Science 18, 539–551 (2008)
Marusak, P.: Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors. Applied Soft Computing 9, 1111–1125 (2009)
Marusak, P., Tatjewski, P.: Effective dual–mode fuzzy DMC algorithms with on-line quadratic optimization and guaranteed stability. International Journal of Applied Mathematics and Computer Science 19, 127–141 (2009)
Marusak, P.: Efficient model predictive control algorithm with fuzzy approximations of nonlinear models. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 448–457. Springer, Heidelberg (2009)
Morari, M., Lee, J.H.: Model predictive control: past, present and future. Computers and Chemical Engineering 23, 667–682 (1999)
Qin, S.J., Badgwell, T.: A survey of industrial model predictive control technology. Control Engineering Practice 11, 733–764 (2003)
Piegat, A.: Fuzzy modelling and control. Physica-Verlag, Heidelberg (2001)
Rossiter, J.A.: Model–Based Predictive Control. CRC Press, Boca Raton (2003)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Systems, Man and Cybernetics 15, 116–132 (1985)
Saez, D., Cipriano, A., Ordys, A.W.: Optimisation of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants. Springer, London (2002)
Tatjewski, P.: Advanced Control of Industrial Processes; Structures and Algorithms. Springer, London (2007)
Tatjewski, P.: Supervisory predictive control and on–line set–point optimization. International Journal of Applied Mathematics and Computer Science 20, 483–495 (2010)
Zanin, A., Tvrzska de Gouvea, M., Odloak, D.: Industrial implementation of a real–time optimization strategy for maximizing production of LPG in a FCC unit. Computers and Chemical Engineering 24, 525–531 (2000)
Zanin, A., Tvrzska de Gouvea, M., Odloak, D.: Integrating real–time optimization into model predictive controller of the FCC system. Computers and Chemical Engineering 10, 819–831 (2002)
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Marusak, P.M. (2011). Efficient Predictive Control and Set–Point Optimization Based on a Single Fuzzy Model. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_23
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DOI: https://doi.org/10.1007/978-3-642-20267-4_23
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