Application of Autocovariance Least - Squares Methods to Laboratory Data ∗
نویسندگان
چکیده
The purpose of this paper is to demonstrate the autocovariance least-squares (ALS) techniques to a laboratory reactor for the conversion of acetic anhydride to acetic acid. In this way, it is demonstrated that the methods proposed by Odelson and Rawlings are applicable to actual process data, and not just theoretical simulations. A variety of control scenarios are tested including: simple regulatory control, setpoint changes, input disturbance rejection, output disturbance rejection, and model mismatch rejection. We demonstrate that updating the disturbance parameters while the model is known sufficiently well, has a significant payoff in terms of control performance. However, we further demonstrate that more significant savings in control performance are realized when the ALS methods are applied to model mismatch cases. For example, when an output disturbance model is used to reject an input disturbance, or when the parameter estimation of a nonlinear model is carried out incorrectly. For the sake of completeness, a PID controller is used for comparison purposes. The goal of this project was not to find a superior or novel control strategy for the acetic anhydride reaction, simply to demonstrate the benefits of the ALS methods on an advanced control strategy. However, the control benefits are seen to be far superior to PID control. 0This technical report is an expanded version of [5], and is also included in [4] 1 TWMCC Technical Report 2003-03 2
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تاریخ انتشار 2003