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Intelligent control of DC motor driven mechanical systems: a robust learning control approach
Author(s) -
Kuc TaeYong,
Baek SeungMin,
Sohn KyungOh,
Kim JinOh
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.702
Subject(s) - control theory (sociology) , controller (irrigation) , dc motor , nonlinear system , stability (learning theory) , computer science , bounding overwatch , control engineering , robust control , mechanical system , lyapunov function , inverse dynamics , iterative learning control , exponential stability , control system , control (management) , engineering , artificial intelligence , machine learning , physics , electrical engineering , kinematics , classical mechanics , quantum mechanics , agronomy , biology
Abstract A robust learning controller is presented for DC motor driven mechanical systems with friction. The proposed controller takes advantage of both robust and learning control approaches to learn and compensate periodic and non‐periodic uncertain dynamics. In the learning controller, a set of learning rules is implemented in which three types of learnings occur: one is direct learning of desired inverse dynamics input and the other two learning of unknown linear parameters and nonlinear bounding functions in the models of system dynamics and friction. The global asymptotic stability of learning control system is shown by using the Lyapunov stability theory. Experimental data demonstrate the effectiveness of developed learning approach to tracking of DC motor driven mechanical systems. Copyright © 2002 John Wiley & Sons, Ltd.