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IEEE Conference on Decision and control

作者： Johan Kon Jeroen van de Wijdeven Dennis Bruijnen Roland Tóth Marcel Heertjes Tom Oomen Departement of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Philips Engineering Solutions Eindhoven The Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology The Netherlands Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft The Netherlands

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-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.

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Learning for Precision Motion of an Interventional X-ray System: Add-on Physics-Guided Neural Network Feedforward control

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IFAC-PapersOnLine 2023年第2期56卷 7523-7528页

作者： Johan Kon Naomi de Vos Dennis Bruijnen Jeroen van de Wijdeven Marcel Heertjes Tom Oomen Control Systems Technology Group Departement of Mechanical Engineering Eindhoven University of Technology P.O. Box 513 5600 MB Eindhoven The Netherlands Philips Engineering Solutions Eindhoven The Netherlands ASML Veldhoven The Netherlands Delft Center for Systems and Control Delft University of Technology The 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.

关键词： Feedforward control physics-guided neural networks interventional X-ray

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Direct Learning for Parameter-Varying Feedforward control: A Neural-Network Approach

arXiv

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arXiv 2023年

作者： Kon, Johan van de Wijdeven, Jeroen Bruijnen, Dennis Tóth, Roland Heertjes, Marcel Oomen, Tom Control Systems Technology Group Departement of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands Philips Engineering Solutions Eindhoven Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft Netherlands

关键词： Learning systems

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Model Predictive control with Model Error Compensation by Koopman Approach 48

Model Predictive Control with Model Error Compensation by Ko...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Kanai, Masaki Yamakita, Masaki R&d Group Hitachi Ltd. Controls and Robotics Innovation Center Ibaraki Japan Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This study proposes a method for high-performance model predictive control (MPC) by using a lifted bilinear model error model (MEM) based on the Koopman approach to compensate for errors in the prediction model. In MPC, more accurate prediction models can be expected to provide better control performance for the real plant. However, there often exist modeling errors in the prediction model, which may prevent MPC from achieving good performance. In this study, we identify the errors in the nominal model as an MEM, and formulate an MPC that integrates the nominal model and the MEM into an "error-compensated model"as a prediction model. We model the MEM with a lifted bilinear model that can represent nonlinear characteristics based on Koopman's theory. In addition, an efficient linear MPC algorithm is constructed by linearizing the MEM while maintaining high prediction accuracy. Numerical simulations demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Model predictive control

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Uncertainty and its Effect on Optimal Multidrug control of Hemodynamic Variables

Uncertainty and its Effect on Optimal Multidrug Control of H...

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American control Conference (ACC)

作者： Teodora Popescu Isabela R. Birs Ghada Ben Othman Erhan Yumuk Marcian Mihai Erwin Hegedus Dana Copot Robin De Keyser Clara M. Ionescu Cristina I. Muresan Department of Automation Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Ghent University Gent Belgium Department of Control and Automation Engineering Istanbul Technical University Maslak Istanbul Turkey

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Computer aided control in biomedical applications is gaining more and more popularity due to numerous research studies that have proven the efficiency of automatic control over manual dosing, which is highly susceptible to human errors. Optimal drug dosing is best achieved using automatic control, which triggers important benefits in terms of both costs and patient side-effects. However, mathematical models for patients are highly susceptible to large modeling uncertainty. A predictive control algorithm is designed in this paper for optimal multidrug control of hemodynamic variables. Improved closed loop performance is obtained compared to similar control strategies, for $\pm 30\%$ modeling uncertainty. The simulation results demonstrate that predictive control is a feasible solution for optimal drug dosing. An analysis of the closed loop performance for significant patient variability shows that controllers tuned using a nominal patient model often fail to achieve desired robustness. To limit the effect of modeling uncertainty, the prediction model should be updated using an online identification tool to extract patient features.

关键词： Drugs Uncertainty Simulation Europe Predictive models Prediction algorithms Mathematical models

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A Negative Imaginary Solution to an Aircraft Platooning Problem

A Negative Imaginary Solution to an Aircraft Platooning Prob...

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European control Conference (ECC)

作者： Yu-Hsiang Su Parijat Bhowmick Alexander Lanzon Department of EEE Control Systems and Robotics Group School of Engineering University of Manchester Manchester UK Department of EEE IIT Guwahati Assam India

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Over the next decade, the growth of commercial aircraft is expected to increase by 30%, causing significant challenges in air traffic management and control. To address this problem, we propose the idea of aircraft platooning during the descending and pre-landing phases. The objective is to design a distributed flight guidance and control system that assists onboard pilots in finding a feasible and collision-free trajectory from descent to pre-landing. The proposed aircraft platoon control scheme comprises a feedback linearising controller in the inner loop that transforms the nonlinear aircraft dynamics into a MIMO double-integrator, inherently a Negative Imaginary system. The outer loop employs a distributed output feedback Strictly Negative Imaginary controller, enabling networked aeroplanes to maintain the desired inter-aircraft spacing along each coordinate by synchronising their velocities. In addition, a contingency strategy is proposed to handle potential runway failures (e.g. sudden blockage, damage, etc.) by switching a descending aircraft platoon into a time-varying hover formation for each aircraft, maintaining a safe vertical gap. Finally, a comprehensive MATLAB simulation case study is conducted to test the feasibility and performance of the NI theory-based aircraft platoon control scheme.

关键词： Contingency management Transforms Switches Aerodynamics Nonlinear dynamical systems Trajectory Air traffic control

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A Linear Parameter-Varying Approach to Data Predictive control

arXiv

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arXiv 2023年

作者： Verhoek, Chris Berberich, Julian Haesaert, Sofie Tóth, Roland Abbas, Hossam S. The Control Systems Group Eindhoven University of Technology Netherlands The University of Stuttgart Institute for Systems Theory and Automatic Control Germany The Institute for Electrical Engineering in Medicine Universität zu Lübeck Germany The Systems and Control Lab Institute for Computer Science and Control Hungary

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system. © 2023, CC BY-NC-SA.

关键词： Predictive control systems

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Evolution Behaviors and Properties of Power Compartment Fires in Underground Urban Utility Tunnels-A Review 6

Evolution Behaviors and Properties of Power Compartment Fire...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Zhou, Jianxin Wang, Yunlong Zheng, Jingrong You, Fei State Grid Electric Power Research Institute Nari Technology Co. Ltd NARI-TECH Nanjing Control Systems Ltd. Nari Group Corporation Nanjing China College of Safety Science and Engineering Institute of Fire Science and Engineering Nanjing Tech University Nanjing China

ISBN: (纸本)9798350346671

In this review, a comprehensive definition for an underground urban utility tunnel UUUT is given to reflect its inherent quality, unique features and latent fire safety issues. Following this, major fire hazards and high fire risks inside power compartments are analyzed. Based on this, international related reported fire accidents are counted and described. Such situation calls for special, systematic and deep researches of these fires. Typical international studies of power compartment fires are hereafter summarized in their main cases, contents, important results and conclusions. However, current researches are far from enough to understand power compartment fires fundamentally and practically. Therefore, seven primary future directions are proposed. © 2023 IEEE.

关键词： Fires

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PID control of data-driven patient response with fixed ratio co-administration of drugs for depth of hypnosis

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IFAC-PapersOnLine 2024年第7期58卷 222-227页

作者： Clara M. Ionescu Hamed Farbakhsh Cristina Muresan Robin De Keyser Erhan Yumuk Ghada Ben Othman Amani Rayene Ynineb Isabela Birs Dana Copot Martine Neckebroek Ghent University Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Tech Lane Science Park 125 9052 Zwijnaarde Belgium Technical University of Cluj-Napoca Department of Automation Memorandumului no. 28 Cluj-Napoca 400114 Romania Istanbul Technical University Department of Control and Automation Engineering Systems and Control Istanbul Turkey Ghent University Hospital Department of Anesthesia C. Heymanslaan 10 9000 Ghent Belgium

In this work we make use of knowledge from clinical practice to deliver a trusted environment for feedback control of co-administration of two drugs to induce depth of hypnosis during general anesthesia. The clinical data we have provides valuable insight into the ratio used to determine the Propofol and Remifentanil infusion rates by means of specifying their corresponding effect site concentrations. A feedback closed loop with a fixed parameter PID controller tuned on population dynamics is used. To account the strong variations in gain of the system (patient) we propose an online identification of the dose-effect response from clinical data available during clinical protocol. Theoretical analysis is provided to justify the approach and simulations with real data from patient are given to support the theoretical insight. Limitations of this approach are discussed as to justify why ratio based co-administration is used in practice solely during the induction phase of general anesthesia.

关键词： Closed-Loop control of Anesthesia PID control Depth of Hypnosis Ratio control Model Estimation Data Driven control

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Accurate \mathcal{H}_{\infty}-Norm Estimation via Finite-Frequency Norms of Local Parametric Models

Accurate \mathcal{H}_{\infty}-Norm Estimation via Finite-Fre...

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2021 American control Conference, ACC 2021

作者： Tacx, Paul Oomen, Tom Control Systems Technology Group Eindhoven University of Technology Departement of Mechanical Engineering Eindhoven5600 Netherlands

ISBN: (纸本)9781665441971

\mathcal{H}_{\infty}-norm estimation is of critical importance for robust control design. This paper aims to develop an algorithm to estimate the \mathcal{H}_{\infty}-norm with a limited amount of data and limited user intervention. A frequency response-based approach is pursued, and local modeling techniques are exploited to include the inter-grid behavior. The main idea is to estimate the global \mathcal{H}_{\infty} norm by estimating the finite-frequency L8norm of the local models through the generalized KYP lemma. A simulation example shows the superiority of the proposed algorithm. © 2021 American Automatic control Council.

关键词： Frequency response

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