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Model predictive control relevant identification and validation
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DOI：10.1016/S0009-2509(03)00077-0.png)
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Abstract
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The role of data prefiltering in model identification and validation is presented in this paper. A model predictive control relevant data prefilter, namely the multistep ahead prediction filter for optimal predictions over every step within a finite horizon, is presented. It is shown that models that minimize the multistep prediction errors can be identified or verified by filtering the data using certain data prefilters and then applying the prediction error method to the filtered data. Based on these identification results, a predictive control relevant model validation scheme using the local approach is proposed. The developed algorithms are verified through simulations as well as industrial applications. (C) 2003 Elsevier Science Ltd. All rights reserved.
Keywords:
model validation
detection of abrupt change
process identification
optimal prediction
prediction error method
model predictive control
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