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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Ahmad, I. M’zoughi, F. Aboutalebi, P. Garrido, I. Garrido, A.J. Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Engineering School of Bilbao—EIB/BIE Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Bilbao Spain

ISBN: (纸本)9781032905570

In the pursuit of sustainable electricity generation from offshore wind and wave energy, the combination of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has emerged as a promising solution to enhance the working lifespan of this kind of systems. This study introduces a control-focused modeling approach, using empirical data collected from the widely accepted FAST environment, to implement advanced control laws for these integrated platforms. Leveraging Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN), the research yields predictive models that effectively capture the platform pitch dynamics so as to optimize control strategies. By integrating advanced machine learning techniques, these approaches address the challenges of modeling floating offshore structures in dynamic environments. In this sense, the neuro-estimators developed in this study closely match the original plant results, offering researchers a straightforward way to implement complex control schemes that could hardly be implemented over the original non-linear system. © 2025 The Author(s).

关键词： Offshore wind turbines

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Vibration Mitigation of Hybrid Oscillating Water Column-Floating Offshore Wind Turbine Using ANFIS-Based Airflow control 16th

Vibration Mitigation of Hybrid Oscillating Water Column-Floa...

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16th APCA International Conference on automatic control and Soft Computing, controlO 2024

作者： M’zoughi, Fares Aboutalebi, Payam Ahmad, Irfan Rouch, Tahereh Bagheri Garrido, Izaskun Garrido, Aitor J. De La Sen, Manuel Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Po Rafael Moreno no. 3 Bilbao48013 Spain Automatic Control Group—ACG Institute of Research and Development of Processes IIDP Department of Electricity and Electronics Faculty of Science and Technology University of the Basque Country—UPV/EHU Bo Sarriena s/n Leioa48080 Spain

ISBN: (纸本)9783031817236

This paper introduces a novel approach to model and stabilize a Floating Offshore Wind Turbine (FOWT) by employing Oscillating Water Columns (OWC) as an active structural control system. The innovative concept involves designing a new floating barge-like platform with integrated OWCs on opposite sides of the platform to mitigate undesired oscillations of the system. These OWCs counteract the bending forces caused by wind on the tower and waves on the barge platform. To synchronize the opposing forces with the system’s tilting, a proposed Adaptive Neuro-Fuzzy Inference System-based airflow control strategy is employed. Through manipulation of the barge platform’s pitch angle, the ANFIS airflow control system adjusts the valves on either side, opening one and closing the other accordingly. Simulation results, compared with a standard FOWT demonstrate the effectiveness of the ANFIS airflow control. It is shown to be superior in reducing platform pitching and the fore-aft translation of the tower top. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Active Structural control Air Valve Airflow control ANFIS Barge Platform Floating Offshore Wind Turbine Oscillating Water Column

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Investigating the role of an AI-based assistance system for the decision-making process of pilots– an interview study

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Transportation Research Procedia 2025年 88卷 167-175页

作者： Angela Menig Nastassja Becker Johannes P. Kleudgen Technical University Darmstadt Research Group of Work and Engineering Psychology Alexanderstraße 10 64283 Darmstadt Germany Technical University Darmstadt Institute of Flight Systems and Automatic Control Otto-Bernd-Str. 2 64287 Darmstadt Germany

The introduction of cockpit assistance systems based on Artificial Intelligence (AI) hold the potential to significantly increase flight safety and efficiency, especially in complex and time-critical situations. However, there is limited understanding how an AI-based system could effectively assist pilots in the decision-making process. This interview study investigates the decision-making process of pilots in a complex decision-making scenario with and without the support of a Level 1B AI-based assistance system (EASA Roadmap 2.0, 2023). In a complex in-flight diversion scenario, the AI-based assistance system (named Virtual Crew Assistant) provides an assessment of the risks and benefits of suitable alternates in a user-friendly interface, enabling a direct comparison of the given alternates. Semi-standardized expert interviews were conducted with 12 commercial aviation pilots. The pilots were asked to perform a decision-making process for a complex scenario - using the conventional FOR-DEC decision-making model - with and without the support of a static prototype of the Virtual Crew Assistant (VCA). During the decision-making process, the experts were instructed to think aloud, and were also requested to rate the workload for each condition. Working with the AI-based system workload was rated significantly lower than working without the AI-based system. In addition, conditions for Trust, Explainable AI, Usability and Limitations of AI-based assistance systems in the cockpit were investigated. Other possible functions of the AI-based system suggested by the experts are given and extending the human-machine interaction to higher collaborative AI-Levels is discussed.

关键词： AI-based assistance system trustworthiness explainable AI decision-making process reduced crew operations workload

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Correction to: Virtual flight deck crew assistance utilizing artificial intelligence methods to interpret NOTAMs: a user acceptance study (CEAS Aeronautical Journal, (2024), 15, 4, (1137-1144), 10.1007/s13272-024-00767-1)

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CEAS Aeronautical Journal 2025年第2期16卷 661-661页

作者： Dieter, Michelle Sprenger, Eric Pasnicu, Otilia Staudt, Josefine Ellenrieder, Nils Institute of Flight Systems and Automatic Control Technical University of Darmstadt Otto-Berndt-Str. 2 Darmstadt64287 Germany Work and Engineering Psychology Research Group Technical University of Darmstadt Alexanderstr. 10 Darmstadt64283 Germany

The article "Virtual fight deck crew assistance utilizing artifcial intelligence methods to interpret NOTAMs: a user acceptance study", written by Michelle Dieter, Eric Sprenger, Otilia Pasnicu, Josefne Staudt and Nils Ellenrieder, was originally published under exclusive license to Deutsches Zentrum für Luft- und Raumfahrt e.V. As a result of the subsequent decision to publish the article under the open access model, the article’s copyright notice was changed on 17 February 2025 to © The Author(s) 2025 and the article is now distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://***/licenses/by/4.0. Open access funding enabled and organized by Projekt DEAL. © The Author(s) 2025.

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System Level Synthesis for Affine control Policies: Model Based and Data-Driven Settings

arXiv

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arXiv 2025年

作者： Schüepp, Lukas De Pasquale, Giulia Dörfler, Florian Alonso, Carmen Amo Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zurich8092 Switzerland Computer Science Department of Stanford University Palo AltoCA United States Control Systems Group Department of Electrical Engineering TU Eindhoven Flux Groene Loper 19 Eindhoven5612AP Netherlands

There is an increasing need for effective control of systems with complex dynamics, particularly through data-driven approaches. System Level Synthesis (SLS) has emerged as a powerful framework that facilitates the control of large-scale systems while accounting for model uncertainties. SLS approaches are currently limited to linear systems and time-varying linear control policies, thus limiting the class of achievable control strategies. We introduce a novel closed-loop parameterization for time-varying affine control policies, extending the SLS framework to a broader class of systems and policies. We show that the closed-loop behavior under affine policies can be equivalently characterized using past system trajectories, enabling a fully data-driven formulation. This parameterization seamlessly integrates affine policies into optimal control problems, allowing for a closed-loop formulation of general Model Predictive control (MPC) problems. To the best of our knowledge, this is the first work to extend SLS to affine policies in both model-based and data-driven settings, enabling an equivalent formulation of MPC problems using closed-loop maps. We validate our approach through numerical experiments, demonstrating that our model-based and data-driven affine SLS formulations achieve performance on par with traditional model-based MPC. © 2025, CC BY.

关键词： Distributed parameter control systems

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Dynamic modelling and genetic-based trajectory generation for non-holonomic mobile manipulators

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control engineering PRACTICE 1997年第1期5卷 39-48页

作者： Chen, MW Zalzala, AMS Robotics Research Group Department of Automatic Control and Systems Engineering The University of Sheffield UK

An approach for the modelling and motion planning of a mobile manipulator system with a non-holonomic constraint is presented in this paper. The Newton-Euler equations are used to obtain the complete dynamics of the system. Given the trajectory of the end-effector of the manipulator, near-optimal trajectories for the mobile platform and manipulator joints are obtained by using an efficient genetic algorithm with torque and manipulability optimisation and obstacle avoidance. Various simulations of a platform with a 3-link onboard manipulator are presented to show the effectiveness of the presented method.

关键词： robotics trajectory planning genetic algorithms dynamics obstacle avoidance

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AN IMPLEMENTATION OF FAST GLOBALLY-STABLE ADAPTIVE ROBOT control ON A MULTIPROCESSOR NETWORK

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control engineering PRACTICE 1995年第11期3卷 1553-1562页

作者： ZIAUDDIN, SM ZALZALA, AMS Department of Automatic Control and Systems Engineering Robotics Research Group University of Sheffield Mappin Street Sheffield Sl 4DU UK

This paper is concerned with efficient and fast computations of adaptive control algorithms for robot manipulators. Adaptive controllers employing the highly coupled and non-linear dynamics of manipulators are computationally complex, which presents a major obstacle in their real-time implementation for industrial applications. A solution to this problem is suggested by utilising a parallel-processing approach. The controller algorithm is implemented for a robot manipulator with six degrees of freedom, utilising a transputer network, for which programming is performed in C. The execution times achieved by the distributed algorithm are well within the limit acceptable for real-time control.

关键词： ADAPTIVE control ROBOTIC MANIPULATORS GLOBAL STABILITY PARALLEL PROCESSING MULTIPROCESSORS

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Distributed Time-Varying Gaussian Regression via Kalman Filtering

arXiv

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arXiv 2025年

作者： Taddei, Nicola Maggioni, Riccardo Eising, Jaap De Pasquale, Giulia Dörfler, Florian Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zurich8092 Switzerland Control Systems Group Department of Electrical Engineering TU Eindhoven Flux Groene Loper 19 Eindhoven5612 AP Netherlands

We consider the problem of learning time-varying functions in a distributed fashion, where agents collect local information to collaboratively achieve a shared estimate. This task is particularly relevant in control applications, whenever real-time and robust estimation of dynamic cost/reward functions in safety critical settings has to be performed. In this paper, we adopt a finite-dimensional approximation of a Gaussian Process, corresponding to a Bayesian linear regression in an appropriate feature space, and propose a new algorithm, DistKP, to track the time-varying coefficients via a distributed Kalman filter. The proposed method works for arbitrary kernels and under weaker assumptions on the time-evolution of the function to learn compared to the literature. We validate our results using a simulation example in which a fleet of Unmanned Aerial Vehicles (UAVs) learns a dynamically changing wind field. Copyright © 2025, The Authors. All rights reserved.

关键词： Gaussian distribution

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A Machine-Learning Approach for the Development of a FOWT Model Integrated with Four OWCs 26

A Machine-Learning Approach for the Development of a FOWT Mo...

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26th International Conference on Circuits, systems, Communications and Computers, CSCC 2022

作者： Ahmad, Irfan Mzoughi, Fares Aboutalebi, Payam Garrido, Izaskun Garrido, Aitor Automatic Control Group-ACG Department of Automatic Control and Systems Engineering University of Basque Country- Upviehu Bilbao Spain

ISBN: (纸本)9781665481861

The wind-wave excitations cause structural vibrations on the Floating Offshore Wind Turbines (FOWT) pressing the power generation efficiency and reducing the life expectancy. In particular, tower-top displacement and barge-type platform pitch dynamics are extremely sensitive to wind speed and wave elevation to the point that may even lead to structural instability in extreme conditions. Having into account that computational techniques such as Artificial Neural Networks (ANNs) are widely used in artificial intelligence because of their strong predicting and forecasting capabilities, the aim of this article is to create a deep-layer ANN model that incorporates Oscillating Water Columns (OWCs) into the barge platform. This ANN model enables to address stability issues of the hybrid floating offshore wind platform. The proposed control-oriented model has been successfully validated to achieve adequate dynamic behavior and structural performance using FAST. © 2022 IEEE.

关键词： Wind

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A Machine-Learning Approach for Prognosis of Oscillating Water Column Wave Generators 27

A Machine-Learning Approach for Prognosis of Oscillating Wat...

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27th International Conference on Circuits, systems, Communications and Computers, CSCC 2023

作者： Garrido, Izaskun Lecube, Jon Mzoughi, Fares Aboutalebi, Pavam Ahmad, Irfan Cayuela, Salvador Garrido, Ailtor University of the Basque Country-UPV/EHU Automatic Control Group Acg Department of Automatic Control and Systems Engineering Bilbao Spain

ISBN: (纸本)9798350337594

Wave excitations cause structural vibrations on the Oscillating Water Columns (OWC) lowering the generated power and reducing the life expectancy. The problem of generator deterioration has been considered for the Mutriku MOWC plant and a machine learning-based approach for prognosis and fault characterization has been proposed. In particular, the use of k-Nearest Neighbors (kNN) models for predicting the time to failure of OWC generators has been proposed. The analysis is based on data collected from sensors that measure various operational parameters of the turbines. The results demonstrate that the proposed kNN model is an excellent choice for reducing maintenance costs by enabling scheduling months in advance. The model's high accuracy in predicting generator failures allows for timely and cost-effective maintenance, preventing costly breakdowns and improving turbine efficiency. These results highlight the potential of machine learning-based approaches for addressing maintenance challenges in the energy sector and underscore the importance of proactive strategies in reducing operational costs and maximizing energy production. © 2023 IEEE.

关键词： Cost effectiveness

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