Dr. Seidl et al., NEURAL-NETWORK COMPENSATION OF GEAR BACKLASH HYSTERESIS IN POSITION-CONTROLLED MECHANISMS, IEEE transactions on industry applications, 31(6), 1995, pp. 1475-1483
This paper demonstrates that artificial neural networks can be used to
identify and compensate for hysteresis caused by gear backlash in pre
cision position-controlled mechanisms. A major contribution of this re
search is that physical analysis of the system nonlinearities and opti
mal control are used to design the neural network structure. Network s
izing and initializing problems are thus eliminated. This physically m
eaningful, modular approach facilitates the integration of this neural
network with existing controllers; thus, initial performance matches
that of existing control approaches and then is improved by refining t
he parameter estimates via further learning. The neural network operat
es by recognizing backlash and switching to a control which moves smoo
thly through the backlash when the torque transmitted to the output sh
aft must be reversed.