MS16.4: Control and Synchronization in Nonlinear Systems

Session Information

Jul 23, 2024 14:00 - 15:20(Europe/Amsterdam)
Venue : AULA - Commissiekamer 2
20240723T1400 20240723T1520 Europe/Amsterdam MS16.4: Control and Synchronization in Nonlinear Systems AULA - Commissiekamer 2 Enoc2024 n.fontein@tudelft.nl Add to Calendar

Sub Sessions

Towards Globally Stable Path-Following Control of Automated Vehicles

MS-16 - Control and Synchronization in Nonlinear Systems 02:00 PM - 02:20 PM (Europe/Amsterdam) 2024/07/23 12:00:00 UTC - 2024/07/23 12:20:00 UTC
The path-following control of automated vehicles is analyzed in the presence of large disturbances. The global dynamics of three controller variations are analyzed using phase portraits, illustrating the domain of attraction of stable path-following, the presence of possible steady state solutions and the transient response of the system. A globally stable control law is proposed, which removes all unwanted steady state solutions, guaranteeing stable path-following regardless of initial conditions and disturbances.
Presenters
IV
Illes Voros
University Of Michigan, Ann Arbor
Co-Authors
DT
Denes Takacs
BME
GO
Gabor Orosz
Professor Of Mechanical Engineering, University Of Michigan, Ann Arbor

Data-driven design of complex network structures to promote synchronization

MS-16 - Control and Synchronization in Nonlinear Systems 02:20 PM - 02:40 PM (Europe/Amsterdam) 2024/07/23 12:20:00 UTC - 2024/07/23 12:40:00 UTC
We consider the problem of optimizing the interconnection graphs of complex networks to enhance synchronization. In instances where traditional optimization methods prove impractical due to uncertain or unknown node dynamics, we advocate for a data-driven approach that exploits available datasets containing pertinent examples. We consider two representative case studies, one featuring linear and the other nonlinear node dynamics. We explored different design strategies and discovered that the most effective ones either rely on using knowledge from examples close to a specific Pareto front or employ a combination of a neural network and a genetic algorithm. Notably, these strategies exhibit statistically superior performance compared to the best examples within the datasets.
Presenters
MC
Marco Coraggio
Postdoctoral Fellow, Scuola Superiore Meridionale
Co-Authors
MD
Mario Di Bernardo
Professor, University Of Naples Federico II

A dominance approach for the analysis of emergent patterns in networks

MS-16 - Control and Synchronization in Nonlinear Systems 02:40 PM - 03:00 PM (Europe/Amsterdam) 2024/07/23 12:40:00 UTC - 2024/07/23 13:00:00 UTC
This note focuses on the analysis of emergent behaviors in networks of dynamical systems, where their interations are of activation-type locally and inhibitory-type in the long-range. Such interplay between activation and inhibition is reminiscent of the classical formula for oscillations in lumped systems, i.e., local positive feedback and long-range negative feedback. Dominance tools are used for the analysis of the resulting behavior, obtaining certificates in the form of linear matrix inequalities which allows to study the existence of emergent static pattern or oscillations in the network. A numerical example illustrates the proposed approach.
Presenters
FM
Félix Miranda Villatoro
Researcher, INRIA

Kernel-based extremum-seeking control for data-efficient performance optimization of nonlinear systems

MS-16 - Control and Synchronization in Nonlinear Systems 03:00 PM - 03:20 PM (Europe/Amsterdam) 2024/07/23 13:00:00 UTC - 2024/07/23 13:20:00 UTC
Extremum-seeking control is a purely data-based performance optimization strategy for dynamical systems. Typical extremum seeking approaches rely on applying perturbations to tunable system parameters and measuring the corresponding system output, to determine a suitable search direction aimed at optimizing steady-state performance. While in practical applications performing measurements may be costly or time-consuming, these measurement data are typically discarded after a single parameter update step. To make more efficient use of these valuable data, we propose a novel extremum-seeking control approach that uses the data to construct an online approximation of the system’s steady-state performance map, instead of discarding them. By using this approximation to determine the search direction and update step size whenever the approximation is deemed sufficiently accurate, the total number of measurements needed to optimize performance is significantly reduced, as shown in an illustrative example.
Presenters
WW
Wouter Weekers
PhD Student, Eindhoven University Of Technology
Co-Authors
AS
Alessandro Saccon
NV
Nathan Van De Wouw
Professor, Eindhoven University Of Technology
258 visits

Session Participants

Online
Session speakers, moderators & attendees
Postdoctoral fellow
,
Scuola Superiore Meridionale
University Of Michigan, Ann Arbor
PhD Student
,
Eindhoven University Of Technology
Professor
,
Eindhoven University Of Technology
Ms. Panagiota Atzampou
PhD Candidate
,
Delft University Of Technology
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Extendend Abstracts

1711533731enoc2024_latex_template.pdf
A dominance approach for the analysis...
2
Submitted by Félix Miranda Villatoro
1705272715enoc2024_data_driven_network_design.pdf
Data-driven design of complex network...
2
Submitted by Marco Coraggio
1706310294ENOC_2024_AV__Illes_-2.pdf
Towards Globally Stable Path-Followin...
2
Submitted by Illes Voros
1712062066Abstract_ENOC_2024.pdf
Kernel-based extremum-seeking control...
4
Submitted by Wouter Weekers

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