Abstract
This paper presents a self-scheduling MPC framework for plants described by LPV (Linear Parameter Varying) models. Such a controller adjusts to variations in plant dynamics by using measured values of the parameters in the control law. We apply the method to control of nonlinear plants approximated by LPV models constructed from multiple local linear models. In this context the parameters constitute model validity functions which are estimated on-line and used for scheduling MPC. Both quadratic programming based finite horizon MPC and min-max type LMI based MPC algorithms are discussed and applied to continuous and batch systems.
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© 1998 Springer Science+Business Media Dordrecht
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Arkun, Y., Banerjee, A., Lakshmanan, N.M. (1998). Self-Scheduling MPC Using LPV Models. In: Berber, R., Kravaris, C. (eds) Nonlinear Model Based Process Control. NATO ASI Series, vol 353. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5094-1_3
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DOI: https://doi.org/10.1007/978-94-011-5094-1_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6140-7
Online ISBN: 978-94-011-5094-1
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