State Estimation and Control of Nonlinear Process Using Neural Networks
نویسنده
چکیده
This paper considers the use of neural networks for non-linear state estimation, identification and control of non-linear processes. The non-linear identification is using feed-forward neural networks as a useful mathematical tool to build a model between the input and the output of a non-linear process. In this paper we consider the possibility of on-line state estimation of the actual parameters from an off-line trained neural state space model of the non-linear process using the gain matrix. This linearization technique is used in the algorithm of on-line tuning of the controller parameters based on pole placement control design for non-linear process.
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