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Causal state-feedback parameterizations in robust model predictive control
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DOI：10.1016/j.automatica.2013.06.015.png)
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Abstract
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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. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Robust model predictive control
Optimization under uncertainties
Relaxation
S-procedure
LMI
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