24 citations · 65 across the 14 of their papers we have counts for
14 papers
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…
Autonomous Hook-Based Grasping and Transportation with Quadcopters
Péter Antal, Tamás Péni, Roland Tóth
Payload grasping and transportation with quadcopters is an active research area that has rapidly developed over the last decade. To grasp a payload without human interaction, most…
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…
On Improved Commutation for Moving-Magnet Planar Actuators
Yorick Broens, Hans Butler, Roland Tóth
The demand for high-precision and high-throughput motion control systems has increased significantly in recent years. The use of moving-magnet planar actuators (MMPAs) is gaining p…
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)…
Finite Dimensional Koopman Form of Polynomial Nonlinear Systems
Lucian Cristian Iacob, Maarten Schoukens, Roland Tóth
The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process, using so-ca…
Learning For Predictive Control: A Dual Gaussian Process Approach
Yuhan Liu, Pengyu Wang, Roland Tóth
An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable contr…
Direct data-driven LPV control of nonlinear systems: An experimental result
Chris Verhoek, Hossam S. Abbas, Roland Tóth
We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) syste…
Direct Data-Driven State-Feedback Control of Linear Parameter-Varying Systems
Chris Verhoek, Roland Tóth, Hossam S. Abbas
The framework of linear parameter-varying (LPV) systems has shown to be a powerful tool for the design of controllers for complex nonlinear systems using linear tools. In this work…
Deep Subspace Encoders for Nonlinear System Identification
Gerben I. Beintema, Maarten Schoukens, Roland Tóth
Using Artificial Neural Networks (ANN) for nonlinear system identification has proven to be a promising approach, but despite of all recent research efforts, many practical and the…
Continuous-time identification of dynamic state-space models by deep subspace encoding
Gerben I. Beintema, Maarten Schoukens, Roland Tóth
Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time…