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This paper extends earlier work on nonlinear predictive control based on a representation of the nonlinear plant as quasi-LPV model. Since the scheduling parameters depend on state variables and inputs, they can be predicted. An efficient predictive scheme with guaranteed stability is proposed that involves solving a sequence of SOCP problems at each sampling period. Compared with previously reported...
Nonlinear Model Predictive Control often suffers from excessive computational complexity, which becomes critical when fast plants are to be controlled. This papers presents an approach to NMPC that exploits the quasi-LPV framework. For quasi-LPV systems, the scheduling variables are determined by the state variables and/or inputs. By calculating an estimate of the state variables during prediction,...
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