2015
DOI: 10.1016/j.ifacol.2015.09.307
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Gain-Scheduled MPC Design for Nonlinear Systems with Input Constraints∗∗The work has been supported by the Slovak Scientific Grant Agency VEGA, Grant No. 1/1241/12.

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Cited by 3 publications
(1 citation statement)
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“…Generally speaking this is not much of a concern in the process industry, where the dynamics of systems are relatively slow allowing for more computationally intensive control approaches (Gros et al, 2020). For nonlinear systems with faster dynamics, where the range of operation requires nonlinearity to be accounted for, adaptive MPC (Adetola et al, 2009) and gain scheduled MPC (Ilka and Veseleý, 2015;Wiid et al, 2021) can be used.…”
Section: Linear Vs Nonlinear Mpcmentioning
confidence: 99%
“…Generally speaking this is not much of a concern in the process industry, where the dynamics of systems are relatively slow allowing for more computationally intensive control approaches (Gros et al, 2020). For nonlinear systems with faster dynamics, where the range of operation requires nonlinearity to be accounted for, adaptive MPC (Adetola et al, 2009) and gain scheduled MPC (Ilka and Veseleý, 2015;Wiid et al, 2021) can be used.…”
Section: Linear Vs Nonlinear Mpcmentioning
confidence: 99%