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- W4240743212 abstract "Model predictive controllers (MPCs) have many features. They are multivariable controllers with model-based predictive capability. They continuously optimize the process by rigorously planning and executing small movement in manipulated variables (MVs). As simple architecture, they have data collection modules, control variable prediction modules, steady state optimization modules, and dynamic optimization modules. The process starts with reading the current value of controlled and manipulated variables and using its internal process model to predict the future value of controlled variables (CVs). In every execution, MPC reconcile this prediction value with actual process measurements to compensate for model inaccuracies. Also, they calculate the size of the control process (i.e., number of MV and CV available for control purpose) in every execution to see whether the panel operator made any change in size. Its steady-state optimization module then calculates the optimum targets for CV and MV and feeds this information to dynamic module to plan detail MV movement to achieve those targets. Depending on various tuning parameters and MV-CV limits, the dynamic module initially plans for a series of MV movements so that those targets can be achieved and the process can be brought to its most economic optimum zone. The first step of MV movement is actually implemented through PID controllers, and all other moves are discarded. In the next execution, again all the calculations are repeated." @default.
- W4240743212 created "2022-05-12" @default.
- W4240743212 date "2017-08-23" @default.
- W4240743212 modified "2023-09-26" @default.
- W4240743212 title "Theoretical Base of MPC" @default.
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- W4240743212 doi "https://doi.org/10.1002/9781119243434.ch2" @default.
- W4240743212 hasPublicationYear "2017" @default.
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