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An integrated approach for machine-learning-based system identification of dynamical systems under control:application towards the model predictive control of a highly nonlinear reactor system

引用

Frontiers of Chemical Science and Engineering 2022年第2期16卷 237-250页

作者： ewan chee Wee Chin Wong Xiaonan Wang Department of Chemical&Biomolecular Engineering Faculty of EngineeringNational University of SingaporeSingapore 117585Singapore Chemical Engineering&Food Technology Cluster Singapore Institute of TechnologySingapore 138683Singapore

Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output *** quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast *** work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive *** showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor *** procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating *** controllers also had reasonable per-iteration times of ca.0.1 *** demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.

关键词： nonlinear model predictive control black-box modeling continuous-time system identification machine learning industrial applications of process control

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