Abstract
The direct application of a neural model in Model Predictive Control (MPC) algorithms results in a nonlinear, in general non-convex, optimisation problem which must be solved on-line. A linear approximation of the model for the current operating point can be used for prediction in MPC, but for significantly nonlinear processes control accuracy may be not sufficient. MPC algorithm in which the neural model is linearised on-line along a trajectory is discussed. The control policy is calculated from a quadratic programming problem, nonlinear optimisation is not necessary. Accuracy and computational burden of the algorithm are demonstrated for a high-purity high-pressure distillation column.
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References
Biegler, L.T., Grossmann, I.E.: Retrospective on optimization. Computers and Chemical Engineering 28, 1169–1192 (2004)
Grossmann, I.E., Biegler, L.T.: Part II. Future perspective on optimization. Computers and Chemical Engineering 28, 1193–1218 (2004)
Haykin, S.: Neural networks–a comprehensive foundation. Prentice Hall, Englewood Cliffs (1999)
Henson, M.A.: Nonlinear model predictive control: current status and future directions. Computers and Chemical Engineering 23, 187–202 (1998)
Ławryńczuk, M.: On improving accuracy of computationally efficient nonlinear predictive control based on neural models. Chemical Engineering Science 66, 5253–5267 (2011)
Ławryńczuk, M.: A family of model predictive control algorithms with artificial neural networks. International Journal of Applied Mathematics and Computer Science 17, 217–232 (2007)
Ławryńczuk, M., Tatjewski, P.: An Efficient Nonlinear Predictive Control Algorithm with Neural Models and Its Application to a High-Purity Distillation Process. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 76–85. Springer, Heidelberg (2006)
Maciejowski, J.M.: Predictive control with constraints. Prentice Hall, Harlow (2002)
Martinsen, F., Biegler, L.T., Foss, B.A.: A new optimization algorithm with application to nonlinear MPC. Journal of Process Control 14, 853–865 (2004)
Morari, M., Lee, J.H.: Model predictive control: past, present and future. Computers and Chemical Engineering 23, 667–682 (1999)
Nørgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural networks for modelling and control of dynamic systems. Springer, London (2000)
Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11, 733–764 (2003)
Tatjewski, P.: Advanced control of industrial processes. Structures and Algorithms. Springer, London (2007)
Tatjewski, P., Ławryńczuk, M.: Soft computing in model-based predictive control. International Journal of Applied Mathematics and Computer Science 16, 101–120 (2006)
Wächter, A., Biegler, L.T.: On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Mathematical Programming 106, 25–57 (2006)
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Ławryńczuk, M. (2012). On-Line Trajectory-Based Linearisation of Neural Models for a Computationally Efficient Predictive Control Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_15
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DOI: https://doi.org/10.1007/978-3-642-29347-4_15
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