Identification of Multivariable Industrial Processes: for by Yucai Zhu PhD, Ton Backx PhD (auth.)

By Yucai Zhu PhD, Ton Backx PhD (auth.)

Identification of Multivariable business Processes offers a unified method of multivariable business strategy identity. It concentrates on commercial approaches with regards to version purposes. The components lined are scan layout, version constitution choice, parameter estimation in addition to blunders bounds of the move functionality. This booklet is meant to fill the space among smooth structures and keep an eye on concept and commercial program. it really is in accordance with the result of 10 years of analysis and alertness reports. The theories and versions mentioned are absolutely defined and illustrated with case experiences. At an early degree the reader is brought to actual applications.

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Extra info for Identification of Multivariable Industrial Processes: for Simulation, Diagnosis and Control

Sample text

U(t) vet) yet) f: (t) Fig. 2 Error generation in transfer operator estimation The advantage of paramettic identification by the least-squares method, or equation error method, is its numerical simplicity. The method has a closed solution. 8) is minimized. It will work well when the noise level is low and model order is correct. 4. This will cause accuracy problems if the noise level increases. 4. This is of course not desired in applications such as 45 control and simulation. The extension of the method to multivariable processes is straightforward if diagonal form MFD models (see Chapter 2) are used.

The causes of them are in most cases known. Usually it is not possible to prevent drifts and slow variations during open loop experiments. Trends and drifts in general have bad influence on the estimation results. They do not average out because of their low frequency behaviour and they will cause considerable bias of the model. On the other hand. this type of disturbance can easily be compensated for by the feedback control. Hence if the model is used for control design, there is no need to model this part of the signals.

The method is easy to comprehend and. due to the existence of a closed solution. it is also easy to implement. The least-squares method is also called linear regression (in statistical literature) and equation error method (in identification literature). 2). Before making assumptions and pursuing a theoretical analysis of the method. we first test the method on two industrial processes. 3) where also the method is extended to a multivariable case. The method is successful for the first process; but it fails for the second one.

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