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Neural network sliding mode robot control
Karel Jezernik, Miran Rodič, Riko šafarič, Boris Curk
Journal:

Robotica / Volume 15 / Issue 1 / January 1997

Published online by Cambridge University Press:

01 January 1997, pp. 23-30
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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.