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Neural network sliding mode robot control

Published online by Cambridge University Press: 01 January 1997

Karel Jezernik
Affiliation:
Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia. E-mail: karel.jezernik@uni-mb.si
Miran Rodič
Affiliation:
Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia. E-mail: karel.jezernik@uni-mb.si
Riko šafarič
Affiliation:
Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia. E-mail: karel.jezernik@uni-mb.si
Boris Curk
Affiliation:
Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia. E-mail: karel.jezernik@uni-mb.si

Abstract

This paper develops a method for neural network control design with sliding modes in which robustness is inherent. Neural network control is formulated to become a class of variable structure (VSS) control. Sliding modes are used to determine best values for parameters in neural network learning rules, thereby robustness in learning control can be improved. A switching manifold is prescribed and the phase trajectory is demanded to satisfy both, the reaching condition and the sliding condition for sliding modes.

Type
Research Article
Copyright
© 1997 Cambridge University Press

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