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Personal profile

Research profile

Pepijn Cox was born on 12th January 1989 in Arnhem, The Netherlands. He has received his B.Sc. degree in Mechanical Engineering with distinction in 2010 and his M.Sc. degree in Systems and Control Engineering with distinction in 2013, both at the Delft University of Technology, the Netherlands. His M.Sc. topic was on “Verification of Cascading Events in Interconnected Stochastic Systems: Using Adaptive Parametric Importance Sampling Method”.

In 2013, he started his Ph.D. project at the Control Systems group at Eindhoven University of Technology (TU/e), The Netherlands. His Ph.D. topic was "Towards Efficient Identification of Linear Parameter-Varying State-Space Models" under the supervision of prof.dr.ir. P.M.J. Van den Hof and dr.ir. R. Tóth. During the period 2013-2014, he took graduate courses at the Dutch Institute of Systems and Control (DISC) and received the DISC certificate. Currently, he is a postdoctoral researcher in the Control Systems group at TU/e. Pepijn Cox’s main research interests are in linear parameter-varying (LPV) and nonlinear system modelling and identification.

Fingerprint Dive into the research topics where Pepijn B. Cox is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Identification (control systems) Engineering & Materials Science
Scheduling Engineering & Materials Science
Impulse response Engineering & Materials Science
Maximum likelihood Engineering & Materials Science
Model Order Reduction Mathematics
Subspace Methods Mathematics
Maximum likelihood estimation Engineering & Materials Science
Computational complexity Engineering & Materials Science

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Research Output 2014 2018

3 Citations (Scopus)
41 Downloads (Pure)

Affine parameter-dependent Lyapunov functions for LPV systems with affine dependence

Cox, P. B., Weiland, S. & Toth, R., 1 Nov 2018, In : IEEE Transactions on Automatic Control. 63, 11, p. 3865-3872

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
Lyapunov functions
Linear matrix inequalities
Linear systems
Robust stability
1 Downloads (Pure)

LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods

Schulz, E., Cox, P. B., Toth, R. & Werner, H., 18 Jan 2018, 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Piscataway: Institute of Electrical and Electronics Engineers, Vol. 2018-January. p. 3575-3581 7 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Model Order Reduction
Subspace Methods
Identification (control systems)
State Space

Prediction-error identification of LPV systems : a nonparametric Gaussian regression approach: A nonparametric Gaussian regression approach

Darwish, M. A. H., Cox, P. B., Proimadis, I., Pillonetto, G. & Toth, R., Nov 2018, In : Automatica. 97, p. 92-103 12 p.

Research output: Contribution to journalArticleAcademicpeer-review

Networked control systems
Batch reactors

Towards efficient identification of linear parameter-varying state-space models

Cox, P. B., 20 Mar 2018, Eindhoven: Technische Universiteit Eindhoven. 341 p.

Research output: ThesisPhd Thesis 1 (Research TU/e / Graduation TU/e)

Open Access
Identification (control systems)

Towards efficient maximum likelihood estimation of LPV-SS models

Cox, P. B., Tóth, R. & Petreczky, M., 1 Nov 2018, In : Automatica. 97, p. 392-403 12 p.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
Maximum likelihood estimation
Maximum likelihood
Impulse response
Dynamical systems

Student theses

Sensing and control design for high speed autonomous agile manoeuvring with quad-rotors

Author: Kiriouchine, V., 2018

Supervisor: Toth, R. (Supervisor 1), Cox, P. (Supervisor 2) & Steinbuch, M. (Supervisor 2)

Student thesis: Master