JK
Johan Kon
Authored
11 records found
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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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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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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Control-relevant neural networks for feedforward control with preview
Applied to an industrial flatbed printer
The performance of feedforward control depends strongly on its ability to compensate for reproducible disturbances. The aim of this paper is to develop a systematic framework for artificial neural networks (ANN) for feedforward control. The method involves three aspects: a new cr
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Neural Network Training Using Closed-Loop Data
Hazards and an Instrumental Variable (IVNN) Solution
An increasing trend in the use of neural networks in control systems is being observed. The aim of this paper is to reveal that the straightforward application of learning neural network feedforward controllers with closed-loop data may introduce parameter inconsistency that degr
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Learning for Precision Motion of an Interventional X-ray System
Add-on Physics-Guided Neural Network Feedforward Control
Tracking performance of physical-model-based feedforward control for interventional X-ray systems is limited by hard-to-model parasitic nonlinear dynamics, such as cable forces and nonlinear friction. In this paper, these nonlinear dynamics are compensated using a physics-guided
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Cross-coupled iterative learning control
A computationally efficient approach applied to an industrial flatbed printer
Cross-coupled iterative learning control (ILC) can improve the contour tracking performance of manufacturing systems significantly. This paper aims to develop a framework for norm-optimal cross-coupled ILC that enables intuitive tuning of time- and iteration-varying weights of th
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Cross-coupled iterative learning control
A computationally efficient approach applied to an industrial flatbed printer
Cross-coupled iterative learning control (ILC) can improve the contour tracking performance of manufacturing systems significantly. This paper aims to develop a framework for norm-optimal cross-coupled ILC that enables intuitive tuning of time- and iteration-varying weights of th
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Intermittent Sampling in Repetitive Control
Exploiting Time-Varying Measurements
The performance increase up to the sensor resolution in repetitive control (RC) invalidates the standard assumption in RC that data is available at equidistant time instances, e.g., in systems with package loss or when exploiting timestamped data from optical encoders. The aim of
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Repetitive control can lead to high performance by attenuating periodic disturbances completely, yet it may amplify non-periodic disturbances. The aim of this paper is to achieve both fast learning and low errors in repetitive control. To this end, a nonlinear learning filter is
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