1,721,003 research outputs found
How auxiliary variables and plant data collection affect closed-loop performance of inferential control
No full textThe design of property estimators for inferential control is addressed in this paper, and the effects of the auxiliary variables (estimator's inputs) and of the approach to collect plant data, used to compute the model coefficients, are investigated. The concept of steady-state closed-loop consistency, which is the ability of an estimator to guarantee low offset in the unmeasured controlled variables, is adopted and theoretical results about this property are derived. It is shown how the selection of auxiliary variables represents the most crucial design step that determines the final closed-loop performance of an inferential control system. When this selection is done on a steady-state closed-loop consistency basis, the closed-loop performance is satisfactory, and it is secondary how the dataset is built. On the other hand, when "inconsistent" inputs are used, the performance is, in general, poor and may be significantly affected (in positive or in negative) by the dataset characteristics. © 2007 Elsevier Ltd. All rights reserved
How to Use Simplified Dynamics in Model Predictive Control of Superfractionators
No full textIn this paper, issues associated with the application of model predictive control algorithms to the product quality control of superfractionator columns are addressed. Full-order and reduced- order (pseudo-ramp) models are compared in different predictive control algorithms, and the effects of different disturbance models on the closed-loop performance are emphasized. The results show that the reduced-order model, which is easier to identify, can give excellent results when it is used along with an effective disturbance model and can be implemented with a relatively small sampling time. The results also show that the output disturbance model typically used by industrial algorithms, is the source of poor closed-loop performance, and although the input disturbance model represents a better choice, the rotation factor disturbance model used with the reduced model can represent a simple and relatively effective practical alternative
Consistency of property estimators in distillation column control
No full textThis paper addresses the problem of input selection in inferential control of multicomponent distillation columns by introducing the concept of consistency. An estimator is consistent when a feedback (multivariable) control system that uses the estimates of the controlled variables guarantees low closed-loop steady-state offset in the true unmeasured controlled variables when disturbances enter the system (and/or set-point changes are considered). A definition of this property is given, and the relations between the estimator consistency and the closed-loop steady-state offset are derived for both single-input-single-output and multi-input-multioutput systems. A multicomponent distillation column case study is presented to show that the selection of the most "precise" inputs does not necessarily guarantee the lowest closed-loop offset in the presence of disturbances, whereas the use of less precise but more "consistent" inputs leads to a well-designed estimator that guarantees a lower closed-loop steady-state offset
Un metodo di monitoraggio della prestazione di controllori predittivi basato sull’errore di predizione
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A Prediction Error Based Method for Performance Monitoring of Model Predictive Controllers
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Model Predictive Control for Optimal Oral Anticoagulant Drug Administration
No full textn this article an MPC algorithm was proposed for the optimal oral anticoagulant drug administration. The algorithm uses a second-order discrete-time model of the response of coagulation key parameter (INR) to warfarin, which is adapted to the actual patient’s dynamic response during the first period of therapy. A state and disturbance estimator is used to correct the model prediction with feedback information from INR measurement. Then, the steady-state weekly sequence of war- farin is calculated that keeps INR as close to its desired target as possible. Finally, the warfarin doses for the first week (from the day when INR is measured) are computed by a dynamic optimization module, which assumes that the steady-state weekly sequence is used from the second week. 25 clinical tests have been performed with very promising results
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