TO
T.A.E. Oomen
Authored
20 records found
Nonlinear Bayesian Identification for Motor Commutation
Applied to Switched Reluctance Motors
Switched Reluctance Motors (SRMs) enable power-efficient actuation with mechanically simple designs. This paper aims to identify the nonlinear relationship between torque, rotor angle, and currents, to design commutation functions that minimize torque ripple in SRMs. This is achi
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Direct Learning for Parameter-Varying Feedforward Control
A Neural-Network Approach
The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-dr
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Design for interaction
Factorized Nyquist based control design applied to a Gravitational Wave detector
Gravitational Wave detectors require feedback control to control the length between the sensitive components of the detector. The degrees of freedom in the control system are inherently coupled and the level of interaction furthermore varies over time. A systematic control design
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Fault Detection for Precision Mechatronics
Online Estimation of Mechanical Resonances
The condition of mechatronic production equipment slowly deteriorates over time, increasing the risk of failure and associated unscheduled downtime. A key indicator for an increased risk for failures is the shifting of resonances. The aim of this paper is to track the shifting re
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Position-Dependent Snap Feedforward
A Gaussian Process Framework
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, w
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Unifying Model-Based and Neural Network Feedforward
Physics-Guided Neural Networks with Linear Autoregressive Dynamics
Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is p
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Compensating torque ripples in a coarse pointing mechanism for free-space optical communication
A Gaussian process repetitive control approach
Actuators that require commutation algorithms, such as the switched reluctance motor (SRM) considered in this paper and employed in the coarse pointing assembly (CPA) for free-space optical communication, often have torque-ripple disturbances that are periodic in the commutation-
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Automated MIMO Motion Feedforward Control
Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation
Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimize
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Frequency Response Function Identification from Incomplete Data
A Wavelet-based Approach
Frequency Response Function (FRF) identification plays a crucial role in the design, the control, and the analysis of complex dynamical systems, including thermal and motion systems. Especially for applications that require long measurements, missing data samples, e.g., due to in
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Gaussian Process based Feedforward Control for Nonlinear Systems with Flexible Tasks
With Application to a Printer with Friction
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 unknown structure using input-output data, which can be used to
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Feedforward Control in the Presence of Input Nonlinearities
A Learning-based Approach
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applicat
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Repetitive Control for Lur'e-Type Systems
Application to Mechanical Ventilation
Repetitive control (RC) has shown to achieve superior rejection of periodic disturbances. Many nonlinear systems are subject to repeating disturbances. The aim of this article is to develop a continuous-time RC design with stability guarantees for nonlinear Lur'e-type systems. Ap
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Gaussian process repetitive control
Beyond periodic internal models through kernels
Repetitive control enables the exact compensation of periodic disturbances if the internal model is appropriately selected. The aim of this paper is to develop a novel synthesis technique for repetitive control (RC) based on a new more general internal model. By employing a Gauss
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Cross-Coupled Iterative Learning Control for Complex Systems
A Monotonically Convergent and Computationally Efficient Approach
Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the us
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Physics-Guided Neural Networks for Feedforward Control
An Orthogonal Projection-Based Approach
Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics. To address the unknown dynamics, a physics-based feedforward model
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Frequency Domain Identification of Multirate Systems
A Lifted Local Polynomial Modeling Approach
Frequency-domain representations of multirate systems are essential for controller design and performance evaluation of multirate systems and sampled-data control. The aim of this paper is to develop a time-efficient closed-loop identification approach for multirate systems in th
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Data-enabled predictive control with instrumental variables
The direct equivalence with subspace predictive control
Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable no
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Identifying Lebesgue-sampled Continuous-time Impulse Response Models
A Kernel-based Approach
Control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control. Some of these applications use measurement devices that sample equidistantly in the amplitude domain. The
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Feedforward Control in the Presence of Input Nonlinearities
With Application to a Wirebonder
The increasing demands on throughput and accuracy of semiconductor manufacturing equipment necessitates accurate feedforward motion control that includes compensation of input nonlinearities. The aim of this paper is to develop a data-driven feedforward approach consisting of a W
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Reset-free data-driven gain estimation
Power iteration using reversed-circulant matrices
A direct data-driven iterative algorithm is developed to accurately estimate the H∞ norm of a linear time-invariant system from continuous operation, i.e., without resetting the system. The main technical step involves a reversed-circulant matrix that can be evaluated in a model-
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