In this section
@article{Tahir:2013:10.1016/j.automatica.2013.06.015,
author = {Tahir, F and Jaimoukha, IM},
doi = {10.1016/j.automatica.2013.06.015},
journal = {Automatica},
pages = {2675--2682},
title = {Causal state-feedback parameterizations in robust model predictive control},
url = {http://dx.doi.org/10.1016/j.automatica.2013.06.015},
volume = {49},
year = {2013}
}
TY - JOUR
AB - In this paper, we investigate the problem of nonlinearity (and non-convexity) typically associated with linear state-feedback parameterizations in the Robust Model Predictive Control (RMPC) for uncertain systems. In particular, we propose two tractable approaches to compute an RMPC controller–consisting of both a causal, state-feedback gain and a control-perturbation component–for linear, discrete-time systems involving bounded disturbances and norm-bounded structured model-uncertainties along with hard constraints on the input and state. Both the state-feedback gain and the control-perturbation are explicitly considered as decision variables in the online optimization while avoiding nonlinearity and non-convexity in the formulation. The proposed RMPC controller–computed through LMI optimizations–is responsible for steering the uncertain system state to a terminal invariant set. Numerical examples from the literature demonstrate the advantages of the proposed scheme
AU - Tahir,F
AU - Jaimoukha,IM
DO - 10.1016/j.automatica.2013.06.015
EP - 2682
PY - 2013///
SP - 2675
TI - Causal state-feedback parameterizations in robust model predictive control
T2 - Automatica
UR - http://dx.doi.org/10.1016/j.automatica.2013.06.015
UR - http://hdl.handle.net/10044/1/15307
VL - 49
ER -