NEURAL-NETWORK COMPENSATION OF GEAR BACKLASH HYSTERESIS IN POSITION-CONTROLLED MECHANISMS

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.