Zobrazeno 1 - 10
of 6 656
pro vyhledávání: '"Oomen, A."'
The sampling rate of input and output signals is known to play a critical role in the identification and control of dynamical systems. For slow-sampled continuous-time systems that do not satisfy the Nyquist-Shannon sampling condition for perfect sig
Externí odkaz:
http://arxiv.org/abs/2410.19629
In this paper, we show how $K$-nearest neighbor ($K$-NN) resampling, an off-policy evaluation method proposed in \cite{giegrich2023k}, can be applied to simulate limit order book (LOB) markets and how it can be used to evaluate and calibrate trading
Externí odkaz:
http://arxiv.org/abs/2409.06514
Many systems are subject to periodic disturbances and exhibit repetitive behaviour. Model-based repetitive control employs knowledge of such periodicity to attenuate periodic disturbances and has seen a wide range of successful industrial implementat
Externí odkaz:
http://arxiv.org/abs/2408.15210
Publikováno v:
IEEE Transactions on Automatic Control (2024)
Model inversion is a fundamental technique in feedforward control. Unstable inverse models present a challenge in that useful feedforward control trajectories cannot be generated by directly propagating them. Stable inversion is a process for generat
Externí odkaz:
http://arxiv.org/abs/2404.09845
Block coordinate descent is an optimization technique that is used for estimating multi-input single-output (MISO) continuous-time models, as well as single-input single output (SISO) models in additive form. Despite its widespread use in various opt
Externí odkaz:
http://arxiv.org/abs/2404.09071
Autor:
van Haren, Max, Tsurumoto, Kentaro, Mae, Masahiro, Blanken, Lennart, Ohnishi, Wataru, Oomen, Tom
Iterative learning control (ILC) is capable of improving the tracking performance of repetitive control systems by utilizing data from past iterations. The aim of this paper is to achieve both task flexibility, which is often achieved by ILC with bas
Externí odkaz:
http://arxiv.org/abs/2403.02039
Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the prese
Externí odkaz:
http://arxiv.org/abs/2402.14374
Switched Reluctance Motors (SRMs) are cost-effective electric actuators that utilize magnetic reluctance to generate torque, with torque ripple arising from unaccounted manufacturing defects in the rotor tooth geometry. This paper aims to design a ve
Externí odkaz:
http://arxiv.org/abs/2402.01216
Autor:
Kon, Johan, van de Wijdeven, Jeroen, Bruijnen, Dennis, Tóth, Roland, Heertjes, Marcel, Oomen, Tom
Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e.g., through solvi
Externí odkaz:
http://arxiv.org/abs/2401.10052
When identifying electrical, mechanical, or biological systems, parametric continuous-time identification methods can lead to interpretable and parsimonious models when the model structure aligns with the physical properties of the system. Traditiona
Externí odkaz:
http://arxiv.org/abs/2401.01263