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arXiv 2023年

作者： Kon, Johan de Vos, Naomi Bruijnen, Dennis de Wijdeven, Jeroen van Heertjes, Marcel Oomen, Tom Control Systems Technology Group Departement of Mechanical Engineering Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands Philips Engineering Solutions Eindhoven Netherlands ASML Veldhoven Netherlands Delft Center for Systems and Control Delft University of Technology Netherlands

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 neural network (PGNN), consisting of a physical model, embedding prior knowledge of the dynamics, in parallel with a neural network to learn hard-to-model dynamics. To ensure that the neural network learns only unmodelled effects, the neural network output in the subspace spanned by the physical model is regularized via an orthogonal projection-based approach, resulting in complementary physical model and neural network contributions. The PGNN feedforward controller reduces the tracking error of an interventional X-ray system by a factor of 5 compared to an optimally tuned physical model, successfully compensating the unmodeled parasitic dynamics. Copyright © 2023, The Authors. All rights reserved.

关键词： Feedforward control

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Live Demo: E2P-Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P-Events to Polarization Reconstruction from PD...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Delbruck, Tobi Wang, Zuowen Mei, Haiyang Haessig, Germain Joubert, Damien Haque, Justin Chen, Yingkai Milde, Moritz B. Gruev, Viktor Univ. of Zurich and ETH Zurich Sensors Group Institute of Neuroinformatics Switzerland Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria Western Sydney University Intl. Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Dept. of Electrical and Computer Engineering UrbanaIL United States

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [... 详细信息

ISBN: (纸本)9798350302493

关键词： Polarization

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A Compartmental Modelling Framework for Drug Distribution in Lean and Obese Patients in Long-Term General Anesthesia*

A Compartmental Modelling Framework for Drug Distribution in...

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European control Conference (ECC)

作者： Amani R. Ynineb Erhan Yumuk Hamed Farbakhsh Ghada Ben Othman Dana Copot Isabela R. Birs Cristina I. Muresan Robin De Keyser Samir Ladaci Clara M. Ionescu Martine Neckebroek Department of Electromechanics Ghent University Systems and Metal Engineering Research Group on Dynamical Systems and Control Tech Lane Science Park 125 Zwijnaarde Belgium Department of Control and Automation Engineering Technical University of Istanbul Maslak Istanbul Turkey Department of Automation Technical University of Cluj Napoca Romania Cluj Romania Department of Automatics and Control Engineering National Polytechnic School Algiers Algeria Department of Anesthesia Ghent University Hospital Ghent Belgium

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In personalized medicine applications such as general anesthesia, an individualised pharmacokinetic (PK) model requires to move away from the classical assumption of homogeneous drug mixing in various tissue compartments in the body. By default, these model coefficients are pre-surgery initialized from population based models as a function of age, gender, weight, height, lean body mass and do not include at this moment specific drug diffusion patterns in the fat compartment, whereas various types of fat will induce various time constants in non-lean patients. In this work, the pharmacokinetic compartmental model structure is revisited to account for non-uniform distribution of uptake/clearance time constants in patients as a nonlinear function of body mass index. Simulations are confirming expected patterns of drug distribution in the body and can account for post-anesthesia side effects up to 72 hours. The model is a novel advance in providing the control community with yet increasingly realistic patient models for closed loop control of anesthesia.

关键词： Drugs Precision medicine Sociology Surgery Europe Tail Anesthesia

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Comparative analysis of bioinspired algorithms for tuning a flatness-based controller: A ball and beam case study

Comparative analysis of bioinspired algorithms for tuning a ...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： M. A. Pastrana José Oniram de A. Limaverde Filho Luiz Henrique Oliveira Jaider Bautista Eugênio Fortaleza Daniel M. Muñoz Department of Mechanical Engineering Automation and Control Group (GRACO) University of Brasília Brasília Brazil Department of Electrical Engineering University of Brasília Brasília Brazil Faculty of Engineering Systems Undergraduate Program University of the Amazonia Florencia Colombia

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

This paper provides a comprehensive comparative analysis of the performance of four bioinspired algorithms (Differential Evolution, Grasshopper Optimization Algorithm, Moth-flame Optimization, and Particle Swarm Optimization) in designing a flatness-based controller for a ball and beam system simulated in MATLAB/Simulink environment. The assessment involves the tuning of feedback gains utilizing four distinct fitness functions: Integral Square Error, Integral Absolute Error, Integral Time Absolute Error, and an ad-hoc cost function. Hypothesis tests are employed to choose the optimal controllers from each combination for every bioinspired algorithm and a fitness function. Subsequently, a control performance analysis of these controllers for the ball and beam system is conducted, focusing on critical parameters such as overshoot, settling time, and steady-state error. This analysis demonstrates that all the controllers achieve a zero steady-state error. Noteworthy is the flatness-based controller tuned with the bioinspired Grasshopper Optimization Algorithm and utilizing the Integral Time Absolute Error fitness function, which stands out for achieving the fastest response with the lowest overshoot. This particular controller emerges as the most recommended choice for the specified application.

关键词： Particle beams Focusing Cost function MIMO Steady-state Performance analysis Particle swarm optimization MATLAB Tuning Computational intelligence

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A Data-Driven Distributed control Scheme: Learning Multi-Objective Agent-Based MPC for Path-Tracking

A Data-Driven Distributed Control Scheme: Learning Multi-Obj...

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International Conference on Intelligent Transportation

作者： Jiaming Zhong Reza Valiollahi Mehrizi Yash Vardhan Pant Amir Khajepour Department of Mechanical and Mechatronics Engineering Mechatronic Vehicle Systems (MVS) Lab University of Waterloo Waterloo ON Canada Department of Electrical and Computer Engineering Control Learning and Logic (CL2) group University of Waterloo Waterloo ON Canada

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Agent-based model predictive control (AMPC) has recently been proposed for vehicle systems with various controllers, such as differential braking and torque vectoring, where controllers are regarded as distributed agents contributing to the same objective. However, this scheme is challenging in handling multiple conflicting objectives with coupled agents. A common approach for such tasks is the integrated MPC, where all objectives and agents are stacked together in one optimization. Nevertheless, as more agents and objectives are involved, the integrated MPC will face challenges like computational burdens and maintenance difficulties in practice. To this end, this paper proposes a learning multi-objective AMPC that can improve design flexibility and computing efficiency. First, under the assumption of information exchange, a multi-objective AMPC tailored from the alternating direction method of multipliers (ADMM) is proposed to decouple the system and achieve the same performance as the integrated scheme iteratively. Second, a learning-based method for initializing iterations is proposed to accelerate convergence. In addition, a data management method is proposed for real-time efficiency, and an authentication module is designed for learning reliability. We compare the proposed scheme against the integrated scheme via a combined path-tracking simulation for autonomous vehicles with various controllers. The proposed scheme achieves the same control performance as the integrated one while reducing the computational time by 43.5 %. Furthermore, the learning-based method saves 88.6% more computational time than without learning, making it suitable for real-time implementation.

关键词： Learning systems Torque Decentralized control Reliability engineering Real-time systems Convex functions Planning Information exchange Convergence Predictive control

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Physics–Guided Neural Networks for Feedforward control: From Consistent Identification to Feedforward controller Design*

Physics–Guided Neural Networks for Feedforward Control: Fro...

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IEEE Conference on Decision and control

作者： Max Bolderman Mircea Lazar Hans Butler Department of Electrical Engineering Control Systems Group Eindhoven University of Technology The Netherlands ASML Veldhoven The Netherlands

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Model–based feedforward control improves tracking performance of motion systems if the model describing the inverse dynamics is of sufficient accuracy. Model sets, such as neural networks (NNs) and physics–guided neural networks (PGNNs) are typically used as flexible parametrizations that enable accurate identification of the inverse system dynamics. Currently, these (PG)NNs are used to identify the inverse dynamics directly. However, direct identification of the inverse dynamics is sensitive to noise that is present in the training data, and thereby results in biased parameter estimates which limit the achievable tracking performance. In order to push performance further, it is therefore crucial to account for noise when performing the identification. To address this problem, this paper proposes a forward system identification using (PG)NNs from noisy data. Afterwards, two methods are proposed for inverting PGNNs to design a feedforward controller. The developed methodology is validated on a real–life industrial linear motor, where it showed significant improvements in tracking performance with respect to the direct inverse identification.

关键词： Training System dynamics Tracking Dynamics Training data Feedforward neural networks System identification

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On feedforward control using physics-guided neural networks: Training cost regularization and optimized initialization

arXiv

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arXiv 2022年

作者： Bolderman, Max Lazar, Mircea Butler, Hans Control Systems Group department of Electrical Engineering Eindhoven University of Technology Netherlands ASML Veldhoven Netherlands

Performance of model–based feedforward controllers is typically limited by the accuracy of the inverse system dynamics model. Physics–guided neural networks (PGNN), where a known physical model cooperates in parallel with a neural network, were recently proposed as a method to achieve high accuracy of the identified inverse dynamics. However, the flexible nature of neural networks can create overparameterization when employed in parallel with a physical model, which results in a parameter drift during training. This drift may result in parameters of the physical model not corresponding to their physical values, which increases vulnerability of the PGNN to operating conditions not present in the training data. To address this problem, this paper proposes a regularization method via identified physical parameters, in combination with an optimized training initialization that improves training convergence. The regularized PGNN framework is validated on a real–life industrial linear motor, where it delivers better tracking accuracy and extrapolation. Copyright © 2022, The Authors. All rights reserved.

关键词： Feedforward control

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Stability-Preserving Model Reduction of Networked Lur'e systems

Stability-Preserving Model Reduction of Networked Lur'e Syst...

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European control Conference (ECC)

作者： Yangming Dou Xiaodong Cheng Jacquelien M. A. Scherpen Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands Department of Plant Science Mathematical and Statistical Methods Group (Biometris) Wageningen University & Research Wageningen Netherlands

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper proposes a model reduction approach for simplifying the interconnection topology of Lur'e network systems. A class of reduced-order models are generated by the projection framework based on graph clustering, which not only preserve the network structure but also ensure absolute stability. Furthermore, we provide an upper bound on the input- output approximation error between the original and reduced- order Lur'e network systems, which is expressed as a function of the characteristic matrix of graph clustering. Finally, the results are illustrated via a numerical example.

关键词： Upper bound Network topology Solids Reduced order systems Stability analysis Trajectory Topology

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Design of Robust Output Feedback controllers for Discrete-Time systems with Polynomial Uncertainties

Design of Robust Output Feedback Controllers for Discrete-Ti...

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International Conference on systems and control (ICSC)

作者： Robert Dehnert Sabine Lerch Michelle Rosik Andreas Rauh Bernd Tibken Institute of Automatic Control School of Electrical Information and Media Engineering University of Wuppertal Wuppertal Germany Group: Distributed Control in Interconnected Systems Carl von Ossietzky Universität Oldenburg Oldenburg Germany

This paper addresses the robust dynamic output feedback (DOF) problem for linear parameter-varying (LPV) discrete-time systems with polynomial dependence of uncertainties. For this purpose, the robust control problem is formulated via linear matrix inequalities (LMI) using Lyapunov's theory for a stability proof. Two relaxation methods are required to solve the robust LMI problem. The first is necessary to expand the Lyapunov stability condition to the structured linear control (SLC) problem for DOF controllers. The second type of relaxation is based on the theorem of Ehlich and Zeller and is required to include the polynomial dependence of uncertainties in the LMI design. These two relaxation methods yield an iterative LMI optimization approach. In this paper, we consider the specific example of a robust I-PD controller with a predefined decay rate that satisfies $H$ ∞ conditions for a polynomial parameter-dependent uncertain system.

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Open-Source Chinese Power System with High Spatial and Temporal Resolution 5

Open-Source Chinese Power System with High Spatial and Tempo...

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5th International Conference on Energy, Electrical and Power engineering, CEEPE 2022

作者： Li, Bo Chen, Minyou Zhong, Haiwang Kammen, Daniel M. Li, Xuyang Chongqing University School of Electrical Engineering Chongqing China Tsinghua University State Key Lab of Control and Simulation of Power Systems and Generation Equipment Beijing China Unversity of California Berkeley Energy and Resource Group Berkeley United States

ISBN: (纸本)9781665479059

Generation Expansion Planning (GEP), electricity market, and grid operation of the power system require a high spatial and temporal resolution model. However, limited data availability is the main obstacle to building a power system model with high resolution. This paper presents a method of constructing an open-source test power system with a high spatial resolution for transmission line topology and generation infrastructures and temporal resolution for variable renewable energy (VRE) sources and electricity demand. In addition, investment and operating cost information of existing infrastructures are summarized. A high-resolution open-source test power system representing China is created only by using publicly available data. The open-source Chinese power system can be used for the GEP model to investigate the low-carbon transition of the power system. © 2022 IEEE.

关键词： Renewable energy sources Power engineering Power transmission lines Energy resolution Power systems Topology Planning

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