Control of Nonlinear Chemical Processes Using Neural Models and Feedback Linearization
نویسندگان
چکیده
Black-box modeling techniques based on artificial neural networks are opening new horizons for modeling and controlling nonlinear processes in biotechnology and chemical process industries. The link between dynamic process models and actual process control is provided by the concept of model based control (MBC), e.g. Internal Model Control (IMC) or Model Based Predictive Control (MBPC). To avoid time consuming calculations, feedback linearization techniques can be used to linearize the nonlinear process model. The resulting linear model then can be used in a linear MBC scheme, allowing standard linear control techniques to be applied. Two methods of input/output feedback linearization are described in combination with the use of neural process models. The exact input/output feedback linearization and the approximate input/output feedback linearization. The proposed methods are applied to a MISO (multi-input single-output) laboratory scale pressure process, which shows good results compared to conventional linear techniques.
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