20 citations · 38 across the 6 of their papers we have counts for
7 papers
Exponential stability of data-driven nonlinear MPC based on input/output models
Lea Bold, Irene Schimperna, Karl Worthmann +1
We consider nonlinear model predictive control (MPC) schemes without stabilizing terminal conditions, where the model used in the optimization step is generated based on input-outp…
Stability of data-driven Koopman MPC with terminal conditions
Irene Schimperna, Lea Bold, Johannes Köhler +2
This paper derives conditions under which Model Predictive Control (MPC) with terminal conditions, using a data-driven surrogate model as a prediction model, asymptotically stabili…
Data-driven Model Predictive Control: Asymptotic Stability despite Approximation Errors exemplified in the Koopman framework
Irene Schimperna, Karl Worthmann, Manuel Schaller +2
In this paper, we analyze stability of nonlinear model predictive control (MPC) using data-driven surrogate models in the optimization step. First, we establish asymptotic stabilit…
Offset-free Nonlinear MPC with Koopman-based Surrogate Models
Irene Schimperna, Lea Bold, Karl Worthmann
In this paper, we design offset-free nonlinear Model Predictive Control (MPC) for surrogate models based on Extended Dynamic Mode Decomposition (EDMD). The model used for predictio…
Kernel EDMD for data-driven nonlinear Koopman MPC with stability guarantees
Lea Bold, Manuel Schaller, Irene Schimperna +1
Extended dynamic mode decomposition (EDMD) is a popular data-driven method to predict the action of the Koopman operator, i.e., the evolution of an observable function along the fl…
Robust constrained nonlinear Model Predictive Control with Gated Recurrent Unit model -- Extended version
Irene Schimperna, Lalo Magni
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…
Robust offset-free constrained Model Predictive Control with Long Short-Term Memory Networks -- Extended version
Irene Schimperna, Lalo Magni
This paper develops a control scheme, based on the use of Long Short-Term Memory neural network models and Nonlinear Model Predictive Control, which guarantees recursive feasibilit…