Handling sensor malfunctions in control of particulate processes |
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Authors: | Adiwinata Gani Panagiotis D Christofides |
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Affiliation: | a Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA b Department of Chemical Engineering, McMaster University, Hamilton, Ont., Canada L8S 4L7 |
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Abstract: | This work focuses on feedback control of particulate processes in the presence of sensor data losses. Two typical particulate process examples, a continuous crystallizer and a batch protein crystallizer, modeled by population balance models (PBMs), are considered. In the case of the continuous crystallizer, a Lyapunov-based nonlinear output feedback controller is first designed on the basis of an approximate moment model and is shown to stabilize an open-loop unstable steady-state of the PBM in the presence of input constraints. Then, the problem of modeling sensor data losses is investigated and the robustness of the nonlinear controller with respect to data losses is extensively investigated through simulations. In the case of the batch crystallizer, a predictive controller is first designed to obtain a desired crystal size distribution at the end of the batch while satisfying state and input constraints. Subsequently, we point out how the constraints in the predictive controller can be modified as a means of achieving constraint satisfaction in the closed-loop system in the presence of sensor data losses. |
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Keywords: | Population balance model Model reduction Lyapunov-based control Predictive control Input constraints Sensor malfunctions Crystallization processes |
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