Abstract
The performance of model-based controller depends on the quality of the identified model. Accurate detection of the channel with model-plant mismatch can avoid re-identification of the entire multivariable system, thereby reducing the disturbance to normal production caused by identification test. A model-plant mismatch detection methodology for nonlinear systems based on LPV (Linear Parameter Varying) model was proposed in this work. The detection was performed only when the control performace becomes worse. Firstly, the LPV model based on multi-model interpolation was adopted to represent the nonlinear process. Then partial correlation coefficients between the model residuals and the inputs of the models at each of the operation points were analyzed to diagnose the model-plant mismatch of the local models. Finally, the LPV model was re-identified by updating the local mismatch models and re-estimating the model weighing parameters. The experimental results show that the partial correlation coefficient of the mismatch model is obviously larger than that of the exact model, which can point out the channel with model-plant mismatch correctly.The proposed method is suitable for the nonlinear processes which have relative steady states in their operating trajectorys.
Highlights
With the development of automatic control technology, the control algorithm based on process model, such as MPC (Model Predictive Control), has been widely used in industrial system control
LPV model based on multi-model interpolation is used to represent the nonlinear system first, and the model-plant mismatch is diagnosed by analyzing the partial correlation coefficients between the model residuals and the system inputs of the local linear models at each of the operating points
A methodology of model-plant mismatch detection for nonlinear processes based on LPV model is proposed in this work
Summary
With the development of automatic control technology, the control algorithm based on process model, such as MPC (Model Predictive Control), has been widely used in industrial system control. A data driven methodology based on the statistical bands of Markov parameters are proposed [4] In this method, the model mismatch is detected by evaluating the bias between the band of the normal case and that of the monitored case. The. LPV model based on multi-model interpolation is used to represent the nonlinear system first, and the model-plant mismatch is diagnosed by analyzing the partial correlation coefficients between the model residuals and the system inputs of the local linear models at each of the operating points. The paper is organized as follows: in Section 2, the problem of model-plant mismatch detection of nonlinear process is discussed and a closed-loop detection methodology based on partial correlation calculation for multi-model based LPV model is proposed.
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