Abstract
In this paper a new adaptive scheme for control of linear systems with unknown memoryless input nonlinearities shall be presented. To do this, we define a useful combination of an artificial neural network (ANN) and a linear system which can represent a large number of nonlinear systems. The input nonlinearities of real plants are not only varying in their parameters but also in their structure. Furthermore, in general, it is expected that the input nonlinearity is not only an isolated static effect, like a dead-zone, but a combination of different types of nonlinear effects. A key feature of our new scheme is that the ANN can describe several different types of nonlinear functions without structural changes. Thus, this cascade connection will be very flexible and powerful for different regulating plants. To control the linear part of the system, an adaptive LQ-controller is used with the ANN compensating the static nonlinearity.
Published Version
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