Abstract
This paper is concerned with the position control of a flexible manupilator using a combination consisting of artificial neural networks (ANNs) and a nonlinear adaptive controller (NAC). A class of Hammerstein models is introduced to describe the dynamics of the flexible manipulator by taking into account the friction and dead-zone nonlinearities in its driving system. For the control of such a Hammerstein system the application of an inverse of the static input nonlinearity is proposed for compensation, whereas a linear adaptive controller is used for the resulting dynamic system. In the proposed new scheme, an ANN is applied instead of a fixed inverse nonlinearity. The key feature of this approach is that the ANN can describe several types of nonlinear functions without structural changes. To control the linear part of the system, an adaptive LQ controller is installed.
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