Intelligent modeling, observation, and control for nonlinear systems

In this paper, we present identification methods for nonlinear mechatronic systems. First, we consider a system consisting of a known linear part and an unknown static nonlinearity, With this approach, using an intelligent ob server, it is possible to identify the nonlinear characteristic and to esti mate all unmeasurable system states. The identification result of the nonli nearity and the estimated system states are used to improve the controller performance. Secondly, the first approach is extended to systems where both the linear p arameters and the nonlinear characteristic are unknown. This is achieved by implementing the intelligent observer as a structured recurrent neural net work.