Causal state-feedback parameterizations in robust model predictive control |
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Authors: | Furqan Tahir Imad M. Jaimoukha |
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Affiliation: | Control and Power Group, Department of Electrical and Electronic Engineering, Imperial College, London SW7-2AZ, UK |
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Abstract: | 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. |
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Keywords: | Robust model predictive control Optimization under uncertainties Relaxation S-procedure LMI |
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