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Robust constrained nonlinear Model Predictive Control with Gated Recurrent Unit model☆
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DOI：10.1016/j.automatica.2023.111472.png)
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Abstract
En 中文
In this paper we propose a robust Model Predictive Control where a Gated Recurrent Unit network model is used to learn the input-output dynamics of the system under control. Robust satisfaction of input and output constraints and recursive feasibility in presence of model uncertainties are achieved using a constraint tightening approach. Moreover, new terminal cost and terminal set are introduced in the Model Predictive Control formulation to guarantee Input-to-State Stability of the closed loop system with respect to the uncertainty term.(c) 2023 Published by Elsevier Ltd.
Keywords:
Nonlinear models
Control of constrained systems
Robust control of nonlinear systems
Optimal controller synthesis for systems
with uncertainties
Neural networks technology