237 citations · 553 across the 25 of their papers we have counts for
27 papers
Sparse add-on controller design: A Youla approach to system-level performance
M. van der Hulst, N. Dirkx, R. A. González +3
The performance of high-tech systems is often dictated by a few performance objectives shared among the many closed-loop controlled subsystems operating in the machine, such as syn…
Truncated Gaussian Noise Estimation in State-Space Models
Rodrigo A. González, Angel L. Cedeño, Koen Tiels +1
Within Bayesian state estimation, considerable effort has been devoted to incorporating constraints into state estimation for process optimization, state monitoring, fault detectio…
Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems
Abhishek Chandra, Taniya Kapoor, Mitrofan Curti +2
Complex piezoelectric systems are foundational in industrial applications. Their performance, however, is challenged by the nonlinear voltage-displacement hysteretic relationships.…
Safety and optimality in learning-based control at low computational cost
Dominik Baumann, Krzysztof Kowalczyk, Cristian R. Rojas +2
Applying machine learning methods to physical systems that are supposed to act in the real world requires providing safety guarantees. However, methods that include such guarantees…
Self-Calibrating Position Measurements: Applied to Imperfect Hall Sensors
Max van Meer, Marijn van Noije, Koen Tiels +4
Linear Hall sensors are a cost-effective alternative to optical encoders for measuring the rotor positions of actuators, with the main challenge being that they exhibit position-de…
Magnetic Hysteresis Modeling with Neural Operators
Abhishek Chandra, Bram Daniels, Mitrofan Curti +2
Hysteresis modeling is crucial to comprehend the behavior of magnetic devices, facilitating optimal designs. Hitherto, deep learning-based methods employed to model hysteresis, fac…
A computationally lightweight safe learning algorithm
Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels +1
Safety is an essential asset when learning control policies for physical systems, as violating safety constraints during training can lead to expensive hardware damage. In response…
Neural oscillators for magnetic hysteresis modeling
Abhishek Chandra, Taniya Kapoor, Bram Daniels +4
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems. We d…
Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach
Rodrigo A. González, Koen Tiels, Tom Oomen
Control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control. Some o…
Kernel-based identification using Lebesgue-sampled data
Rodrigo A. González, Koen Tiels, Tom Oomen
Sampling in control applications is increasingly done non-equidistantly in time. This includes applications in motion control, networked control, resource-aware control, and event-…
Discovery of sparse hysteresis models for piezoelectric materials
Abhishek Chandra, Bram Daniels, Mitrofan Curti +3
This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression tec…
Decoupling multivariate functions using a nonparametric filtered tensor decomposition
Jan Decuyper, Koen Tiels, Siep Weiland +2
Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are expressions in the form of basis expansions or neural networks. While highly ef…
PNLSS Toolbox 1.0
Jan Decuyper, Koen Tiels, Johan Schoukens
This is a demonstration of the PNLSS Toolbox 1.0. The toolbox is designed to identify polynomial nonlinear state-space models from data. Nonlinear state-space models can describe a…
Decoupling multivariate functions using a non-parametric Filtered CPD approach
Jan Decuyper, Koen Tiels, Siep Weiland +1
Black-box model structures are dominated by large multivariate functions. Usually a generic basis function expansion is used, e.g. a polynomial basis, and the parameters of the fun…
A nonlinear model of vortex-induced forces on an oscillating cylinder in a fluid flow
Jan Decuyper, Tim De Troyer, Koen Tiels +2
A nonlinear model relating the imposed motion of a circular cylinder, submerged in a fluid flow, to the transverse force coefficient is presented. The nonlinear fluid system, featu…
Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling
Jan Decuyper, Koen Tiels, Mark C. Runacres +1
Nonlinear state-space modelling is a very powerful black-box modelling approach. However powerful, the resulting models tend to be complex, described by a large number of parameter…
Deep Convolutional Networks in System Identification
Carl Andersson, Antônio H. Ribeiro, Koen Tiels +2
Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system…
On the smoothness of nonlinear system identification
Antônio H. Ribeiro, Koen Tiels, Jack Umenberger +2
We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems. We show that for regions of…
Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record
Rishi Relan, Koen Tiels, Anna Marconato +2
Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper,…
A Local Polynomial Approach to Nonparametric Estimation of the Best Linear Approximation of Lithium-Ion Battery From Multiple Datasets
Rishi Relan, Koen Tiels, Jean-Marc Timmermans +1
Battery short-term electrical impedance behavior varies between linear, linear time-varying, or nonlinear at different operating conditions. Data-based electrical impedance modelin…
Structure Discrimination in Block-Oriented Models Using Linear Approximations: A Theoretic Framework
Johan Schoukens, Rik Pintelon, Yves Rolain +5
In this paper we show that it is possible to retrieve structural information about complex block-oriented nonlinear systems, starting from linear approximations of the nonlinear sy…
Nonlinear state-space modelling of the kinematics of an oscillating circular cylinder in a fluid flow
Jan Decuyper, Tim De Troyer, Mark Runacres +2
The flow-induced vibration of bluff bodies is an important problem of many marine, civil, or mechanical engineers. In the design phase of such structures, it is vital to obtain goo…
Comparison of several data-driven nonlinear system identification methods on a simplified glucoregulatory system example
Anna Marconato, Maarten Schoukens, Koen Tiels +3
In this paper, several advanced data-driven nonlinear identification techniques are compared on a specific problem: a simplified glucoregulatory system modeling example. This probl…
Parameter reduction in nonlinear state-space identification of hysteresis
Alireza Fakhrizadeh Esfahani, Philippe Dreesen, Koen Tiels +2
Hysteresis is a highly nonlinear phenomenon, showing up in a wide variety of science and engineering problems. The identification of hysteretic systems from input-output data is a…