Abstract
This paper is concerned with adaptive position control using artificial neural networks (ANNs). The hydraulic system to be investigated consists of a 4/3 way proportional valve, a differential cylinder and a variable load force. This force results from a mass-spring-damper system. The main problem in this configuration is the large dead zone in the valve. Assuming that the cylinder and the load force can be linearly modelled as a second-order system and an integrator, the dynamic model of the hydraulic system can be described as a series connection of a static input non-linearity (dead zone) and a linear system. For the control of such a Hammerstein system, it is proposed to use an inverse of the input non-linearity for compensation and a linear adaptive controller for the resulting system. In our new scheme, we use an ANN instead of a fixed inverse non-linearity. A key feature of this approach is that the ANN can describe several types of non-linear functions without structural changes. To control the linear part of the system, an adaptive LQ controller is used.
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