A receding horizon control algorithm, originally proposed for tracking best-possible steady-states in the presence of overly stringent state and/or input constraints, is analyzed for the case of nonlinear plant models and possibly non-convex cost functionals. Unlike the linear case (with convex cost functionals), convergence to equilibrium is not always possible and only average performance bounds are guaranteed in general.
Receding horizon cost optimization for overly constrained nonlinear plants / Angeli, David; Amrit, Rishi; Rawlings, James B.. - ELETTRONICO. - (2009), pp. 7972-7977. (Intervento presentato al convegno IEEE Conference on Decision and Control) [10.1109/CDC.2009.5400707].
Receding horizon cost optimization for overly constrained nonlinear plants
Angeli, David;
2009
Abstract
A receding horizon control algorithm, originally proposed for tracking best-possible steady-states in the presence of overly stringent state and/or input constraints, is analyzed for the case of nonlinear plant models and possibly non-convex cost functionals. Unlike the linear case (with convex cost functionals), convergence to equilibrium is not always possible and only average performance bounds are guaranteed in general.
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