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A robust model predictive control algorithm for input-output LPV systems using parameter extrapolation
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DOI：10.1016/j.jprocont.2023.103021.png)
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Abstract
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In this paper, we introduce a novel robust Model Predictive Control (MPC) algorithm for Linear Parameter Varying (LPV) systems represented in the Input-Output (IO) form. The proposed scheme embeds integral action and ensures output reference tracking for piece-wise constant signals. The algorithm is based on the online extrapolation of the LPV scheduling parameters, which are generated recursively based on a simple Taylor expansion argument. Closed-loop asymptotic stability, recursive feasibility of the online optimisation, as well as robustness towards bounded disturbances and scheduling parameter prediction uncertainties are demonstrated. Two distinct nonlinear multi-input multi-output benchmarks are used to illustrate the effectiveness of the proposed method: a numeric simulation example, used to compare the method to state-of-the-art techniques, and a high-fidelity twin rotor system, for which the method is further validated. Real-time capabilities of the proposed scheme are highlighted, since it only requires one Quadratic Program to be evaluated per discrete-time sample, during the implementation. & COPY; 2023 Elsevier Ltd. All rights reserved.
Keywords:
Model predictive control
Linear parameter varying systems
Tracking
Parameter estimation
Robust stability
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