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Non‐convex optimization and robustness in realtime model predictive control
Author(s) -
DeHaan Darryl,
Guay Martin
Publication year - 2007
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.1216
Subject(s) - robustness (evolution) , model predictive control , computer science , mathematical optimization , nonlinear system , nonlinear model , control theory (sociology) , convex optimization , regular polygon , control (management) , mathematics , artificial intelligence , biochemistry , chemistry , physics , geometry , quantum mechanics , gene
Recent works in the nonlinear model predictive control (MPC) literature have presented ‘realtime’ optimization approaches based upon incremental updating of input parameters using local descent directions of the cost functional. The main downside to these methods is their strong dependence upon the values used to initialize the input parameters. In this note we study the robustness issues associated with non‐local search methods in continuous‐time MPC, and demonstrate a framework for robustly incorporating these approaches in a realtime setting. Copyright © 2007 John Wiley & Sons, Ltd.