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The design and implementation of a nonlinear model predictive controller (MPC) scheme for an energy-efficient office building is presented.
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This work focuses on the design and implementation of a nonlinear model-based control system on an experimental reverse osmosis (RO) membrane water desalination system in order to deal with large set-point changes and variations in feed water salinity.
The discussion in Higdon (2002) includes the development of the scheme, a linearized stability analysis, a description of some techniques for practical implementation in a nonlinear model, and some numerical testing.
The identification of a nonlinear model and the design of a nonlinear filter are thus avoided.
The fitting was performed in R (http://www.r-project.org) using the nls function, which determines the nonlinear least squares estimates of the parameters in a nonlinear model.
The mathematical model presented here is a nonlinear model of connected growers based on dynamic systems theory.
The first, Model 1, is a linear model, describing transport of YFP, and the second, Model 2, is a nonlinear model describing the osmo-regulation of yeast cells.
Feeding enough observations to a nonlinear model ensures the robustness and precision of plant growth modeling.
Feeding enough and high-quality plant area measures to a nonlinear model (3PLM) ensures the robustness and precision of nonlinear plant growth estimation.
This paper presents the real-time implementation of the Nonlinear Model Predictive Control (NMPC) controller in controlling the temperature in batch esterification process.
Control is obtained by "fast" implementation of Nonlinear Model Predictive Control (NMPC).
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