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Force control of a reciprocating surface grinder using unfalsification and learning concept
Author(s) -
Ali Razavi H.,
Kurfess Thomas R.
Publication year - 2001
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.686
Subject(s) - pulverizer , reciprocating motion , controller (irrigation) , control engineering , set (abstract data type) , control theory (sociology) , control (management) , surface (topology) , computer science , engineering , mechanical engineering , artificial intelligence , mathematics , grinding , agronomy , geometry , biology , programming language , gas compressor
A plant model is an essential requirement in conventional methods for controller synthesis. However, it is possible to find a set of controllers that are not falsified by the performance specification or the measured data without any plant model or prejudicial assumptions. This concept is used to select and implement a force controller for a reciprocating surface grinder. Copyright © 2001 John Wiley & Sons, Ltd.