4 citations · 4 across the 4 of their papers we have counts for
7 papers · 1 filter
Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach
Rishi Ramkannan, Gerben I. Beintema, Roland Tóth +1
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data. To achieve this, it combines the rolled-out nonlinear sta…
Direct data-driven control with signal temporal logic specifications
Birgit C. van Huijgevoort, Chris Verhoek, Roland Tóth +1
Most control synthesis methods under temporal logic properties require a model of the system, however, identifying such a model can be a challenging task. In this work, we develop…
Learning Stable and Robust Linear Parameter-Varying State-Space Models
Chris Verhoek, Ruigang Wang, Roland Tóth
This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for a…
Computationally efficient predictive control based on ANN state-space models
Jan H. Hoekstra, Bence Cseppentő, Gerben I. Beintema +3
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting mode…
Exploring the use of deep learning in task-flexible ILC
Anantha Sai Hariharan Vinjarapu, Yorick Broens, Hans Butler +1
Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforw…
Direct data-driven state-feedback control of general nonlinear systems
Chris Verhoek, Patrick J. W. Koelewijn, Sofie Haesaert +1
Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL)…