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Robust detection of intermittent multiplicative sensor fault.
- Published in:
- Asian Journal of Control, 2021, v. 23, n. 1, p. 463, doi. 10.1002/asjc.2241
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A feedback control framework for safe and economically-optimal operation of nonlinear processes.
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- AIChE Journal, 2016, v. 62, n. 7, p. 2391, doi. 10.1002/aic.15222
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Actuator stiction compensation via model predictive control for nonlinear processes.
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- AIChE Journal, 2016, v. 62, n. 6, p. 2004, doi. 10.1002/aic.15171
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Economic model predictive control of nonlinear time-delay systems: Closed-loop stability and delay compensation.
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- AIChE Journal, 2015, v. 61, n. 12, p. 4152, doi. 10.1002/aic.14964
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On identification of well-conditioned nonlinear systems: Application to economic model predictive control of nonlinear processes.
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- AIChE Journal, 2015, v. 61, n. 10, p. 3353, doi. 10.1002/aic.14942
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- Article
Real-time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control.
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- AIChE Journal, 2015, v. 61, n. 10, p. 3374, doi. 10.1002/aic.14960
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Economic model predictive control of nonlinear process systems using empirical models.
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- AIChE Journal, 2015, v. 61, n. 3, p. 816, doi. 10.1002/aic.14683
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- Article
Real-time economic model predictive control of nonlinear process systems.
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- AIChE Journal, 2015, v. 61, n. 2, p. 555, doi. 10.1002/aic.14673
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Selection of control configurations for economic model predictive control systems.
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- AIChE Journal, 2014, v. 60, n. 9, p. 3230, doi. 10.1002/aic.14514
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Smart manufacturing: Handling preventive actuator maintenance and economics using model predictive control.
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- AIChE Journal, 2014, v. 60, n. 6, p. 2179, doi. 10.1002/aic.14427
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Economic model predictive control with time-varying objective function for nonlinear process systems.
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- AIChE Journal, 2014, v. 60, n. 2, p. 507, doi. 10.1002/aic.14274
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Proactive fault-tolerant model predictive control.
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- AIChE Journal, 2013, v. 59, n. 8, p. 2810, doi. 10.1002/aic.14074
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Modeling and control of protein crystal shape and size in batch crystallization.
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- AIChE Journal, 2013, v. 59, n. 7, p. 2317, doi. 10.1002/aic.14039
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Distributed model predictive control of switched nonlinear systems with scheduled mode transitions.
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- AIChE Journal, 2013, v. 59, n. 3, p. 860, doi. 10.1002/aic.14003
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Composite fast-slow MPC design for nonlinear singularly perturbed systems.
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- AIChE Journal, 2012, v. 58, n. 6, p. 1802, doi. 10.1002/aic.13798
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Economic model predictive control of nonlinear process systems using Lyapunov techniques.
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- AIChE Journal, 2012, v. 58, n. 3, p. 855, doi. 10.1002/aic.12672
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Sequential and Iterative Architectures for Distributed Model Predictive Control of Nonlinear Process Systems.
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- AIChE Journal, 2010, v. 56, n. 8, p. 2137, doi. 10.1002/aic.12155
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Distributed Model Predictive Control of Nonlinear Process Systems.
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- AIChE Journal, 2009, v. 55, n. 5, p. 1171, doi. 10.1002/aic.11801
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Model Predictive Control of Nonlinear Stochastic Partial Differential Equations with Application to a Sputtering Process.
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- AIChE Journal, 2008, v. 54, n. 8, p. 2065, doi. 10.1002/aic.11511
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Enhancing Data-based Fault Isolation Through Nonlinear Control.
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- AIChE Journal, 2008, v. 54, n. 1, p. 223, doi. 10.1002/aic.11328
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Smart Plant Operations: Vision, Progress and Challenges.
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- AIChE Journal, 2007, v. 53, n. 11, p. 2734, doi. 10.1002/aic.11320
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Fault-Tolerant Control of Nonlinear Process Systems Subject to Sensor Faults.
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- AIChE Journal, 2007, v. 53, n. 3, p. 654, doi. 10.1002/aic.11100
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Integrated Fault-Detection and Fault-Tolerant Control of Process Systems.
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- AIChE Journal, 2006, v. 52, n. 6, p. 2130, doi. 10.1002/aic.10806
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A Method for PID Controller Tuning Using Nonlinear Control Techniques.
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- AIChE Journal, 2005, v. 51, n. 12, p. 3292, doi. 10.1002/aic.10566
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Analysis of Mode Transitions in Biological Networks.
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- AIChE Journal, 2005, v. 51, n. 8, p. 2220, doi. 10.1002/aic.10499
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Fault-Tolerant Control of Process Systems Using Communication Networks.
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- AIChE Journal, 2005, v. 51, n. 6, p. 1665, doi. 10.1002/aic.10443
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- Article
Feedback Control of Surface Roughness Using Stochastic PDEs.
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- AIChE Journal, 2005, v. 51, n. 1, p. 345, doi. 10.1002/aic.10299
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Hybrid Predictive Control of Process Systems.
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- AIChE Journal, 2004, v. 50, n. 6, p. 1242, doi. 10.1002/aic.10115
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Coordinating feedback and switching for control of hybrid nonlinear processes.
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- AIChE Journal, 2003, v. 49, n. 8, p. 2079, doi. 10.1002/aic.690490817
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Feedback control of growth rate and surface roughness in thin film growth.
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- AIChE Journal, 2003, v. 49, n. 8, p. 2099, doi. 10.1002/aic.690490818
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Analysis and control of particulate processes with input constraints.
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- AIChE Journal, 2001, v. 47, n. 8, p. 1849, doi. 10.1002/aic.690470815
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Control of nonlinear distributed process systems: Recent developments and challenges.
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- AIChE Journal, 2001, v. 47, n. 3, p. 514, doi. 10.1002/aic.690470302
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Crystal temperature control in the Czochralski crystal growth process.
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- AIChE Journal, 2001, v. 47, n. 1, p. 79, doi. 10.1002/aic.690470111
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Robust control of particulate processes using uncertain population balances.
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- AIChE Journal, 2000, v. 46, n. 2, p. 266, doi. 10.1002/aic.690460207
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Nonlinear control of particulate processes.
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- AIChE Journal, 1999, v. 45, n. 6, p. 1279, doi. 10.1002/aic.690450613
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Feedback control of hyperbolic PDE systems.
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- AIChE Journal, 1996, v. 42, n. 11, p. 3063, doi. 10.1002/aic.690421108
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Editorial to the Special Issue 'Control of Particulate Processes' (Part. Part. Syst. Charact. 4/2008).
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- Particle & Particle Systems Characterization, 2008, v. 25, n. 4, p. 291, doi. 10.1002/ppsc.200890010
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Lyapunov-based Model Predictive Control of Particulate Processes Subject to Asynchronous Measurements.
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- Particle & Particle Systems Characterization, 2008, v. 25, n. 4, p. 360, doi. 10.1002/ppsc.200800030
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- Article
Integrating run-to-run control with feedback control for a spatial atomic layer etching reactor.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2024, v. 203, p. 1, doi. 10.1016/j.cherd.2024.01.030
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- Article
Sparse identification modeling and predictive control of wafer temperature in an atomic layer etching reactor.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2024, v. 202, p. 1, doi. 10.1016/j.cherd.2023.12.024
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Encrypted decentralized model predictive control of nonlinear processes with delays.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2023, v. 200, p. 312, doi. 10.1016/j.cherd.2023.10.041
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Machine learning-based predictive control using on-line model linearization: Application to an experimental electrochemical reactor.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2023, v. 197, p. 721, doi. 10.1016/j.cherd.2023.08.017
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Real-time adaptive sparse-identification-based predictive control of nonlinear processes.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2023, v. 196, p. 750, doi. 10.1016/j.cherd.2023.07.011
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Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2023, v. 191, p. 658, doi. 10.1016/j.cherd.2023.02.003
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On generalization error of neural network models and its application to predictive control of nonlinear processes.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2023, v. 189, p. 664, doi. 10.1016/j.cherd.2022.12.001
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Atomistic-mesoscopic modeling of area-selective thermal atomic layer deposition.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2022, v. 188, p. 271, doi. 10.1016/j.cherd.2022.09.051
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Physics-informed machine learning modeling for predictive control using noisy data.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2022, v. 186, p. 34, doi. 10.1016/j.cherd.2022.07.035
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Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2022, v. 185, p. 87, doi. 10.1016/j.cherd.2022.06.044
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Multivariable run-to-run control of thermal atomic layer etching of aluminum oxide thin films.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2022, v. 182, p. 1, doi. 10.1016/j.cherd.2022.03.039
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Process structure-based recurrent neural network modeling for predictive control: A comparative study.
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- Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2022, v. 179, p. 77, doi. 10.1016/j.cherd.2021.12.046
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