Journal: Control Engineering Practice

Abbreviation

Control Eng. Practice

Publisher

Elsevier

Journal Volumes

ISSN

0967-0661
1873-6939

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Publications1 - 10 of 49
  • A new method for analysis and design of iterative learning control algorithms in the time-domain
    Item type: Journal Article
    Zsiga, Norbert; van Dooren, Stijn; Elbert, Philipp; et al. (2016)
    Control Engineering Practice
  • Model predictive control of high moisture extrusion cooking
    Item type: Journal Article
    Tica, Adrian; Pinnamaraju, Vivek S.; Stirnemann, Eric; et al. (2025)
    Control Engineering Practice
    High Moisture Extrusion Cooking (HMEC) has become a promising technology for producing plant-based meat alternatives. By using HMEC, food manufacturers can create meat-like textures from plant proteins, offering a sustainable solution with reduced carbon footprint to consumers. However, at the current stage of development, the automation level in HMEC is insufficient to ensure operational autonomy, reliability, and product quality expected by industry demands. This paper presents a predictive control framework designed to transform experience-based handled HMEC into a more reliable process operation, improving its production performance and facilitating industrial up-scaling. The proposed control structure is hierarchical, comprising two layers. At the upper layer, a model predictive control (MPC) algorithm determines the optimal set-points for the controllers at the lower layer. The predictive framework is built on the existing HMEC control architecture and can be further extended to achieve fully optimized production. Leveraging linear dynamic models, the approach mainly focuses on the protein melt control aiming to enhance production performance by minimizing the tracking error of process quantities correlated to product quality. The practical feasibility of the designed control solution has been proven on a pilot-scale extruder. Validation results have shown improved operational stability and reproducibility, while effectively tracking set-points for consistent meat-like fibrous structure formation and desired textural characteristics.
  • Model-based iterative learning control strategies for precise trajectory tracking in gasoline engines
    Item type: Journal Article
    Hedinger, Raffael; Zsiga, Norbert; Salazar, Mauro; et al. (2019)
    Control Engineering Practice
  • Data-driven reference trajectory optimization for precision motion systems
    Item type: Journal Article
    Balula, Samuel; Liao-McPherson, Dominic; Rupenyan, Alisa; et al. (2024)
    Control Engineering Practice
    We propose a data-driven optimization-based pre-compensation method to improve the contour tracking performance of precision motion stages by modifying the reference trajectory and without modifying any built-in low-level controllers. The position of the precision motion stage is predicted with data-driven models, a linear low-fidelity model is used to optimize traversal time, by changing the path velocity and acceleration profiles then a non-linear high-fidelity model is used to refine the previously found time-optimal solution. We experimentally demonstrate that the proposed method is capable of simultaneously improving the productivity and accuracy of a high precision motion stage. Given the data-based nature of the models, the proposed method can easily be adapted to a wide family of precision motion systems.
  • Optimal control for chatter mitigation in milling
    Item type: Journal Article
    Monnin, Jérémie; Kuster, Fredy; Wegener, Konrad (2014)
    Control Engineering Practice
  • In Memoriam: Walter Schaufelberger
    Item type: Other Journal Item
    Glattfelder, Doelf; Guzzella, Lino; Kübler, Olaf; et al. (2009)
    Control Engineering Practice
  • Feedback control of Hodgkin-Huxley nerve cell dynamics
    Item type: Journal Article
    Fröhlich, Flavio; Jezernik, Saso (2005)
    Control Engineering Practice
  • Feedback control of particulate matter and nitrogen oxide emissions in diesel engines
    Item type: Journal Article
    Tschanz, Frédéric; Amstutz, Alois; Onder, Christopher H.; et al. (2013)
    Control Engineering Practice
  • Efficient solution of the diesel-engine optimal control problem by time-domain decomposition
    Item type: Journal Article
    Asprion, Jonas; Chinellato, Oscar; Guzzella, Lino (2014)
    Control Engineering Practice
  • System identification beyond the Nyquist frequency: A kernel-regularized approach
    Item type: Journal Article
    van Haren, Max; Smith, Roy; Oomen, Tom (2025)
    Control Engineering Practice
    Models that contain intersample behavior are important for control design of systems with slow-rate outputs. The aim of this paper is to develop a system identification technique for fast-rate models of systems where only slow-rate output measurements are available, e.g., vision-in-the-loop systems. In this paper, the intersample response is estimated by identifying fast-rate models through least-squares criteria, and the limitations of these models are determined. In addition, a method is developed that surpasses these limitations and is capable of estimating unique fast-rate models of arbitrary order by regularizing the least-squares estimate. The developed method utilizes fast-rate inputs and slow-rate output measurements and identifies fast-rate models accurately in a single identification experiment. Finally, both simulation and experimental validation on a prototype wafer stage demonstrate the effectiveness of the framework.
Publications1 - 10 of 49