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Efficient scheduled stabilizing model predictive control for constrained nonlinear systems
Author(s) -
Wan Zhaoyang,
Kothare Mayuresh V.
Publication year - 2003
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.821
Subject(s) - model predictive control , nonlinear system , control theory (sociology) , stability (learning theory) , computer science , nonlinear model , set (abstract data type) , mathematical optimization , control (management) , mathematics , artificial intelligence , machine learning , physics , quantum mechanics , programming language
We present a computationally efficient scheduled model predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local predictive controllers with estimates of their regions of stability covering the desired operating region, and implement them as a single scheduled MPC which on‐line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. This algorithm is computationally efficient and provides a general framework for the scheduled MPC design. The algorithm is illustrated with two examples. Copyright © 2003 John Wiley & Sons, Ltd.