Neural networks for modelling and control of discrete-time nonlinear systems

The modelling and control for a class of SISO discrete-time nonlinear systems is discussed in this paper using multilayered feedforward neural networks (MFNNs). The ability of MFNNs to model arbitrary nonlinear functions is incorporated to approximate the unknown nonlinear I/O relationship and its inverse using a novel learning algorithm. In order to overcome the difficulties associated with simultaneous online identification and control in neural networks based control systems, the new learning control architectures which are based on the Kalman filter equations are developed for control systems with online identification and control ability. The simulation results are also provided.<<ETX>>

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