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Switching linear parameter‐varying control with improved local performance and optimized switching surfaces
Author(s) -
Zhao Pan,
Nagamune Ryozo
Publication year - 2018
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.4088
Subject(s) - control theory (sociology) , particle swarm optimization , controller (irrigation) , computer science , bounded function , function (biology) , mathematical optimization , automotive industry , control (management) , mathematics , engineering , algorithm , mathematical analysis , artificial intelligence , evolutionary biology , agronomy , biology , aerospace engineering
Summary This paper presents a novel approach to designing switching linear parameter‐varying (SLPV) controllers with improved local performance and an algorithm for optimizing switching surfaces to further improve the performance of the SLPV controllers. The design approach utilizes the weighted average of the local L 2 ‐gain bounds (representing the local performance) as the cost function to be minimized, whereas the maximum of the local L 2 ‐gain bounds (representing the worst‐case performance over all subsets) is bounded with a tuning parameter. The tuning parameter is useful for taking the trade‐off between the local performance and the worst‐case performance. An algorithm based on the particle swarm optimization is introduced to optimize the switching surfaces of an SLPV controller. The efficacy of the proposed SLPV controller design approach and switching surface optimization algorithm is demonstrated on both a numerical example and a physical example of air‐fuel ratio control of an automotive engine.