Inverse Dynamics Controllers for Robust Control:
Consequences for Neurocontrollers
Cs. Szepesvári and A. Lõrincz
Proceedings of ICANN'96, Bochum
pp. 697--702
(1996)
Abstract
It is proposed that controllers that approximate the inverse
dynamics of the controlled plant can be used for on-line
compensation of changes in the plant's dynamics. The
idea is to use the very same controller in two modes at the
same time: both for static and dynamic feedback.
Implications for the learning of neurocontrollers are
discussed. The proposed control mode relaxes the demand
of precision and as a consequence, controllers that utilise
direct associative learning by means of local function
approximators may become more tractable in higher
dimensional spaces.
