Robust constrained nonlinear Model Predictive Control with Gated Recurrent Unit model -- Extended version
arXiv:2307.10069 · doi:10.1016/j.automatica.2023.111472
Abstract
In this paper we propose a robust Model Predictive Control where a Gated Recurrent Unit network model is used to learn the input-output dynamic 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.
arXiv commentThis is the extended version of https://doi.org/10.1016/j.automatica.2023.111472
References in corpus (7)
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
- Cautious Model Predictive Control using Gaussian Process Regression
- Performance-oriented model learning for data-driven MPC design
- On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments
- On the stability properties of Gated Recurrent Units neural networks
- Nonlinear MPC design for incrementally ISS systems with application to GRU networks