14 citations · 17 across the 31 of their papers we have counts for
33 papers
Robust Data-Driven Controller Synthesis from Sampled and Uncertain Frequency Response Estimates using Integral Quadratic Constraints
Luuk Marceau Spin, Maurice W. P. M. H. Heemels, Nathan van de Wouw +1
In this paper, we develop a novel MIMO controller synthesis method that relies solely on frequency-domain data of discrete-time systems. The approach is based on an iterative desig…
Iteratively Convex Frequency Data-Driven Synthesis of Structured MIMO Controllers
Luuk Marceau Spin, Thom Samuels, Dragan Kostic +3
Frequency-response-function-based controller design enables synthesis directly from measured data, avoiding the need for accurate parametric plant models. This paper presents a gen…
Uncertainty-based perturb and observe for data-driven optimization
Leontine Aarnoudse, Mark Haring, Nathan van de Wouw +1
Data-based adaptive optimization methods hold great promise for the performance optimization of uncertain, time-varying processes. However, current methods are often based on conti…
Considering Perspectives for Automated Driving Ethics: Collective Risk in Vehicular Motion Planning
Leon Tolksdorf, Arturo Tejada, Christian Birkner +1
Recent automated vehicle (AV) motion planning strategies evolve around minimizing risk in road traffic. However, they exclusively consider risk from the AV's perspective and, as su…
Risk Estimation for Automated Driving
Leon Tolksdorf, Arturo Tejada, Jonas Bauernfeind +2
Safety is a central requirement for automated vehicles. As such, the assessment of risk in automated driving is key in supporting both motion planning technologies and safety evalu…
Uncertainty-Based Perturb and Observe for Fast Optimization of Unknown, Time-Varying Processes
Leontine Aarnoudse, Mark Haring, Nathan van de Wouw +1
Model-free adaptive optimization methods are capable of optimizing unknown, time-varying processes even when other optimization methods are not. However, their practical applicatio…
Collision Probability Estimation for Optimization-based Vehicular Motion Planning
Leon Tolksdorf, Arturo Tejada, Christian Birkner +1
Many motion planning algorithms for automated driving require estimating the probability of collision (POC) to account for uncertainties in the measurement and estimation of the mo…
Efficient Reduction of Interconnected Subsystem Models using Abstracted Environments
Luuk Poort, Bart Besselink, Rob H. B. Fey +1
We present two frameworks for structure-preserving model order reduction of interconnected subsystems, improving tractability of the reduction methods while ensuring stability and…
Abstracted Model Reduction: A General Framework for Efficient Interconnected System Reduction
Luuk Poort, Lars A. L. Janssen, Bart Besselink +2
This paper introduces the concept of abstracted model reduction: a framework to improve the tractability of structure-preserving methods for the complexity reduction of interconnec…
Impact-Aware Control using Time-Invariant Reference Spreading
Jari van Steen, Nathan van de Wouw, Alessandro Saccon
With the goal of increasing the speed and efficiency in robotic manipulation, a control approach is presented that aims to utilize intentional simultaneous impacts to its advantage…
Impact-Aware Robotic Manipulation: Quantifying the Sim-To-Real Gap for Velocity Jumps
Jari van Steen, Daan Stokbroekx, Nathan van de Wouw +1
Impact-aware robotic manipulation benefits from an accurate map from ante-impact to post-impact velocity signals to support, e.g., motion planning and control. This work proposes a…
Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning
Haleh Hayati, Carlos Murguia, Nathan van de Wouw
Federated learning (FL) has emerged as a method to preserve privacy in collaborative distributed learning. In FL, clients train AI models directly on their devices rather than shar…
Model Updating for Nonlinear Systems with Stability Guarantees
Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani +1
To improve the predictive capacity of system models in the input-output sense, this paper presents a framework for model updating via learning of modeling uncertainties in locally…
Reduced-order Modeling of Modular, Position-dependent Systems with Translating Interfaces
Robert A. Egelmeers, Lars A. L. Janssen, Rob H. B. Fey +2
Many complex mechatronic systems consist of multiple interconnected dynamical subsystems, which are designed, developed, analyzed, and manufactured by multiple independent teams. T…
Modular Redesign of Mechatronic Systems: Formulation of Module Specifications Guaranteeing System Dynamics Specifications
Lars A. L. Janssen, Rob H. B. Fey, Bart Besselink +1
Complex mechatronic systems are typically composed of interconnected modules, often developed by independent teams. This development process challenges the verification of system s…
Passivity-Preserving, Balancing-Based Model Reduction for Interconnected Systems
Luuk Poort, Bart Besselink, Rob H. B. Fey +1
This paper proposes a balancing-based model reduction approach for an interconnection of passive dynamic subsystems. This approach preserves the passivity and stability of both the…
Uncertainty Learning for LTI Systems with Stability Guarantees
Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani +1
We present a framework for learning of modeling uncertainties in Linear Time Invariant (LTI) systems. We propose a methodology to extend the dynamics of an LTI (without uncertainty…
Mode Selection for Component Mode Synthesis with Guaranteed Assembly Accuracy
Lars A. L. Janssen, Rob H. B. Fey, Bart Besselink +1
In this work, a modular approach is introduced to select the most important eigenmodes for each component of a composed structural dynamics system to obtain the required accuracy o…
Translating Assembly Accuracy Requirements to Cut-Off Frequencies for Component Mode Synthesis
Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey +1
One of the most popular methods for reducing the complexity of assemblies of finite element models in the field of structural dynamics is component mode synthesis. A main challenge…
Robust Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design
Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani +1
This paper proposes a nonlinear estimator for the robust reconstruction of process and sensor faults for a class of uncertain nonlinear systems. The proposed fault estimation metho…
Quadratic Programming-based Reference Spreading Control for Dual-Arm Robotic Manipulation with Planned Simultaneous Impacts
Jari van Steen, Gijs van den Brandt, Nathan van de Wouw +2
With the aim of further enabling the exploitation of intentional impacts in robotic manipulation, a control framework is presented that directly tackles the challenges posed by tra…
Modular Model Reduction of Interconnected Systems: A Top-Down Approach
Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey +1
Models of complex systems often consist of multiple interconnected subsystem/component models that are developed by multi-disciplinary teams of engineers or scientists. To ensure t…
Immersion and Invariance-based Coding for Privacy in Remote Anomaly Detection
Haleh Hayati, Nathan van de Wouw, Carlos Murguia
We present a framework for the design of coding mechanisms that allow remotely operating anomaly detectors in a privacy-preserving manner. We consider the following problem setup.…
Linear Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design
Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani +1
This paper addresses the problem of robust process and sensor fault reconstruction for nonlinear systems. The proposed method augments the system dynamics with an approximated inte…