paper-with-me

홈 › Papers

Position-Dependent Snap Feedforward: A Gaussian Process Framework

2022-02-01 · Max van Haren, Maurice Poot, Jim Portegies, Tom Oomen

Mechatronic systems have increasingly high performance requirements for motion control. The low-frequency contribution of the flexible dynamics, i.e. the compliance, should be compensated for by means of snap feedforward to achieve high accuracy. Position-dependent compliance, which often occurs in motion systems, requires the snap feedforward parameter to be modeled as a function of position. Position-dependent compliance is compensated for by using a Gaussian process to model the snap feedforward parameter as a continuous function of position. A simulation of a flexible beam shows that a significant performance increase is achieved when using the Gaussian process snap feedforward parameter to compensate for position-dependent compliance.

📄 PDF Abstract BibTeX arXiv:2202.00257

Code (0)

등록된 구현이 없습니다.

Tasks

Position

Methods 이 논문이 사용한 방법론

Gaussian Process Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for…

Similar Papers 제목 키워드 기반

Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

2022-01-19 · Max van Haren, Maurice Poot, Dragan Kostić, Robin van Es 외

Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of …

Gaussian ProcessesPosition

Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes

2023-03-03 · Tim-Lukas Habich, Sarah Kleinjohann, Moritz Schappler

Variable stiffness actuator (VSA) designs are manifold. Conventional model-based control of these nonlinear systems is associated with high effort and design-dependent assumptions. In contrast, machine learning offers a …

Gaussian ProcessesPosition

Learning nonlinear feedforward: a Gaussian Process Approach Applied to a Printer with Friction

2021-12-07 · Max van Meer, Maurice Poot, Jim Portegies, Tom Oomen

Feedforward control is essential to achieving good tracking performance in positioning systems. The aim of this paper is to develop an identification strategy for inverse models of systems with nonlinear dynamics of unkn…

Friction

Diffusion Operator Geometry of Feedforward Representations

2026-05-01 · Kanishka Reddy arxiv

Feedforward neural networks transform data through learned representations whose geometry shapes how classes separate and relate across successive layers. We study that geometry through diffusion operators. Each feature-…

Basis Function feedforward for Position-Dependent Systems

2022-11-01 · Max van Haren, Lennart Blanken, Tom Oomen

Feedforward for motion systems is getting increasingly more important to achieve performance requirements. This leads to a situation where position-dependent effects cannot be neglected anymore.

Position