(195d) Robust Large-Scale State-Estimate Prediction

Effective monitoring and control of a process require online measurements of all state variables of the process. Measurement of all state variables are typically not available due to the lack of reliable online sensors and/or high costs of online sensors. These unmeasured variables are often directly related to product properties, which are important to monitor and control. Their estimates are usually obtained using state estimators. As a result of the need for and the importance of state estimation, the development of accurate state estimators that are robust with respect to model uncertainties and unmeasured inputs, has been the subject of research for decades [1-5]. There have been a few reports on the design of large-scale state estimators [6-11]. Although the implementation of centralized estimators for large-scale systems seems to be an optimal approach, these estimators are not scaleable to complex, large-scale systems, as they suffer from the curse of dimensionality.

In this paper, we address the problem of large-scale robust state-estimate prediction; that is, the prediction of future values of state estimates robustly at every time instant in large-scale processes. To this end, we decompose a large-scale state-estimate prediction problem into a set of smaller-scale state-estimate prediction problems so that the resulting set of the smaller-scale state-estimate predictors are more robust, easier to design and implement, and more computationally efficient. We also study tuning of the smaller-scale state-estimate predictors to maximize the robustness of the predictors set while ensuring the stability of the error dynamics of the entire set. In addition, we address the problem of implementing the smaller-scale state-estimate predictors in parallel (using a computer with parallel processors) to improve computational efficiency of the state estimate prediction while preserving the accuracy and robustness of state-estimate predictions.

References

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