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Data-Driven LIDAR Feedforward Predictive Wind Turbine control

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Data-Driven LIDAR Feedforward Predictive Wind Turbine Contro...

control technology and Applications (CCTA),

作者： Rogier Dinkla Tom Oomen Jan-Willem van Wingerden Sebastiaan P. Mulders Delft Center for Systems and Control Faculty of Mechanical Engineering Delft University of Technology Delft The Netherlands Department of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands

Light Detection and Ranging (LIDAR)-assisted Model Predictive control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework.

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On the Relation between Discrete and Continuous-time Refined Instrumental Variable Methods

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arXiv 2023年

作者： González, Rodrigo A. Rojas, Cristian R. Pan, Siqi Welsh, James S. The Control Systems Technology Group Eindhoven University of Technology Netherlands The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The School of Engineering University of Newcastle Callaghan Australia

The Refined Instrumental Variable method for discrete-time systems (RIV) and its variant for continuous-time systems (RIVC) are popular methods for the identification of linear systems in open-loop. The continuous-time equivalent of the transfer function estimate given by the RIV method is commonly used as an initialization point for the RIVC estimator. In this paper, we prove that these estimators share the same converging points for finite sample size when the continuous-time model has relative degree zero or one. This relation does not hold for higher relative degrees. Then, we propose a modification of the RIV method whose continuous-time equivalent is equal to the RIVC estimator for any non-negative relative degree. The implications of the theoretical results are illustrated via a simulation example. © 2023, CC BY-NC-ND.

关键词： Continuous time systems

Operation Strategies for Modular Large-Scale Alkaline Electrolysis systems

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Operation Strategies for Modular Large-Scale Alkaline Electr...

IEEE International Conference on Industrial Informatics (INDIN)

作者： Hannes Lange Christioph Schirmer Michael Mock Isabell Viedt Anselm Klose Leon Urbas Process Systems Engineering Group TUD Dresden University of Technology Dresden Germany Chair of Process Control Systems TUD Dresden University of Technology Dresden Germany

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In this paper we compare different operation strategies for alkaline electrolyzis (AEL) systems using numeric simulation. We derived general models for mass and energy balances from literature and composed them into a model for an AEL system. Here we considered a numbered-up plant topology, containing multiple AEL stacks with single electrolyte distribution and gas separation systems. The gas impurities and achievable minimal load distribution were compared for the different operating strategies. The results were discussed and further conclusions for the operation of a homogeneous numbered-up AEL system were derived.

关键词： Impurities Electrolytes Numerical models Topology Electrochemical processes Informatics Load modeling

Physics-Informed Learning Using Hamiltonian Neural networks with Output Error Noise Models

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arXiv 2023年

作者： Moradi, Sarvin Jaensson, Nick Tóth, Roland Schoukens, Maarten Control System group Eindhoven University of Technology Eindhoven Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven Netherlands Processing and Performance of Materials Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neural networks (HNNs) implement Hamiltonian theory in deep learning and form a comprehensive framework for modeling autonomous energy-conservative systems. Despite being suitable to estimate a wide range of physical system behavior from data, classical HNNs are restricted to systems without inputs and require noiseless state measurements and information on the derivative of the state to be available. To address these challenges, this paper introduces an Output Error Hamiltonian Neural network (OE-HNN) modeling approach to address the modeling of physical systems with inputs and noisy state measurements. Furthermore, it does not require the state derivatives to be known. Instead, the OE-HNN utilizes an ODE-solver embedded in the training process, which enables the OE-HNN to learn the dynamics from noisy state measurements. In addition, extending HNNs based on the generalized Hamiltonian theory enables to include external inputs into the framework which are important for engineering applications. We demonstrate via simulation examples that the proposed OE-HNNs results in superior modeling performance compared to classical HNNs. © 2023, CC BY-NC-SA.

关键词： Hamiltonians

Direct Data-Driven State-Feedback control of General Nonlinear systems

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Direct Data-Driven State-Feedback Control of General Nonline...

IEEE Conference on Decision and control

作者： Chris Verhoek Patrick J. W. Koelewijn Sofie Haesaert Roland Tóth Dept. of Electrical Engineering Control Systems group Eindhoven University of Technology The Netherlands Institute for Computer Science and Control Budapest Hungary

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc.

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Extending Direct Data-Driven Predictive control Towards systems with Finite control Sets

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Extending Direct Data-Driven Predictive Control Towards Syst...

European control Conference (ECC)

作者： Manuel Klädtke Moritz Schulze Darup Daniel E. Quevedo Department of Mechanical Engineering Control and Cyberphysical Systems Group TU Dortmund University Germany Queensland University of Technology Australia

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Although classical model predictive control with finite control sets (FCS-MPC) is quite a popular control method, particularly in the realm of power electronics systems, its direct data-driven predictive control (FCS-DPC) counterpart has received relatively limited attention. In this paper, we introduce a novel reformulation of a commonly used DPC scheme that allows for the application of a modified sphere decoding algorithm, known for its efficiency and prominence in FCS-MPC applications. We test the reformulation on a popular electrical drive example and compare the computation times of sphere decoding FCS-DPC with an enumeration-based and a MIQP method.

关键词： Europe Prediction algorithms control systems Power electronics Decoding Predictive control

Extending direct data-driven predictive control towards systems with finite control sets

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arXiv 2024年

作者： Klädtke, Manuel Darup, Moritz Schulze Quevedo, Daniel E. The Control and Cyberphysical Systems Group Faculty of Mechanical Engineering TU Dortmund University Germany Queensland University of Technology Australia

关键词： Model predictive control

Plug-in for visualizing 3D tool tracking from videos of Minimally Invasive surgeries

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arXiv 2024年

作者： Nema, Shubhangi Mathur, Abhishek Vachhani, Leena Systems and Control Group Indian Institute of Technology Bombay Maharashtra India Mechanical Engineering Indian Institute of Technology Goa Goa India

Background This paper tackles instrument tracking and 3D visualization challenges in minimally invasive surgery (MIS), crucial for computer-assisted interventions. Conventional and robot-assisted MIS encounter issues with limited 2D camera projections and minimal hardware integration. Method The objective is to track and visualize the entire surgical instrument, including shaft and metallic clasper, enabling safe navigation within the surgical environment. The proposed method involves 2D tracking based on segmentation maps, facilitating creation of labeled dataset without extensive ground-truth knowledge. Geometric changes in 2D intervals express motion, and kinematics based algorithms process results into 3D tracking information. Result Synthesized and experimental results in 2D and 3D motion estimates demonstrate negligible errors, validating the method for labeling and motion tracking of instruments in MIS videos. Conclusion The conclusion underscores the proposed 2D segmentation technique's simplicity and computational efficiency, emphasizing its potential as direct plug-in for 3D visualization in instrument tracking and MIS practices. © 2024, CC BY-SA.

关键词： Computational efficiency

Traffic Sign Detection With Event Cameras and DCNN 25

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Traffic Sign Detection With Event Cameras and DCNN

25th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2022

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Agh University of Science and Technology Computer Vision Laboratory Department of Automatic Control and Robotics Kraków Poland Silesian University of Technology Department of Digital Systems Gliwice Poland

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

In recent years, event cameras (DVS - Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions - parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022 Division of Signal Processing and Electronic systems, Poznan University of technology (DSPES PUT).

关键词： Cameras