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IEEE International Conference on Robotics and Automation (ICRA)

作者： Victor Dhédin Haolong Li Shahram Khorshidi Lukas Mack Adithya Kumar Chinnakkonda Ravi Avadesh Meduri Paarth Shah Felix Grimminger Ludovic Righetti Majid Khadiv Joerg Stueckler Embodied Vision Group Max Planck Institute for Intelligent Systems Tuebingen Germany Movement Generation and Control Group Max Planck Institute for Intelligent Systems Tuebingen Germany Tandon School of Engineering New York University New York USA Oxford Robotics Institute University of Oxford England Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany

Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in particular base height. In this paper, we propose a novel approach for combining visual-inertial odometry (VIO) with leg odometry in an extended Kalman filter (EKF) based state estimator. The VIO module uses a stereo camera and IMU to yield low-drift 3D position and yaw orientation and drift-free pitch and roll orientation of the robot base link in the inertial frame. However, these values have a considerable amount of latency due to image processing and optimization, while the rate of update is quite low which is not suitable for low-level control. To reduce the latency, we predict the VIO state estimate at the rate of the IMU measurements of the VIO sensor. The EKF module uses the base pose and linear velocity predicted by VIO, fuses them further with a second high-rate IMU and leg odometry measurements, and produces robot state estimates with a high frequency and small latency suitable for control. We integrate this lightweight estimation framework with a nonlinear model predictive controller and show successful implementation of a set of agile locomotion behaviors, including trotting and jumping at varying horizontal speeds, on a torque-controlled quadruped robot.

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Learning Stable and Robust Linear Parameter-Varying State-Space Models

Learning Stable and Robust Linear Parameter-Varying State-Sp...

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IEEE Conference on Decision and control

作者： Chris Verhoek Ruigang Wang Roland Tóth Control Systems Group Eindhoven University of Technology The Netherlands Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Australia Vehicle Industry Research Center Szechenyi Istvan University Hungary

This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a user-defined value $\gamma$ . Furthermore, since the parametrizations are direct, the models can be trained using unconstrained optimization. The fact that the trained models are of the LPV-SS class makes them useful for, e.g., further convex analysis or controller design. The effectiveness of the approach is demonstrated on an LPV identification problem.

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Discovery of sparse hysteresis models for piezoelectric materials

arXiv

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arXiv 2023年

作者： Chandra, Abhishek Daniels, Bram Curti, Mitrofan Tiels, Koen Lomonova, Elena A. Tartakovsky, Daniel M. Electromechanics and Power Electronics Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Department of Energy Science and Engineering Stanford University 367 Panama Street StanfordCA94305 United States

This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques. While sparse regression has previously been used to model various scientific and engineering phenomena, its application to nonlinear hysteresis modelling in piezoelectric materials has yet to be explored. The study employs the least-squares algorithm with a sequential threshold to model the dynamic system responsible for hysteresis, resulting in a concise model that accurately predicts hysteresis for both simulated and experimental piezoelectric material data. Several numerical experiments are performed, including learning butterfly-shaped hysteresis and modelling real-world hysteresis data for a piezoelectric actuator. The presented approach is compared to traditional regression-based and neural network methods, demonstrating its efficiency and robustness. Source code is available at https://***/chandratue/SmartHysteresis. © 2023, CC BY.

关键词： Hysteresis

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Robust control of Interleaved Boost Converter for Open-Cathode PEM Fuel Cell systems

Robust Control of Interleaved Boost Converter for Open-Catho...

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IEEE International Conference on Automatic control and Intelligent systems (I2CACIS)

作者： Jie Ying Tan R.M.T Raja Ismail Institute of Postgraduate Studies Universiti Malaysia Pahang Pekan Pahang Malaysia Robotics Intelligent Systems & Control Engineering (RISC) Research Group Faculty of Electrical & Electronics Universiti Malaysia Pahang Pekan Pahang Malaysia

This paper implements a thermal control and a super-twisting sliding mode (STSM) incorporated with an interleaved boost converter (IBC) for an open-cathode proton exchange membrane fuel cell (OC-PEMFC). The implementation of the thermal control is to regulate the stack temperature and adjust the air stoichiometric during the fuel cell current variation. The STSM controller is designed to ensure the fuel cell system’s robustness by achieving the reference values set. Therefore, simulation results discussed that the thermal control provides the suitable stack temperature to the system and maintains the fan voltage during temperature variations. A PI controller is designed and used as a comparison to the proposed STSM controller. Hence, the proposed STSM demonstrates its effectiveness in tracking down the reference current values for the fuel cell system.

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LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

arXiv

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arXiv 2022年

作者： de Lange, Matthis H. Verhoek, Chris Preda, Valentin Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands ESTEC European Space Agency Noordwijk2200AG Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert a nonlinear model of the atmospheric flight dynamics of an GPRV to a Linear Parameter-Varying (LPV) description, such that the LPV model is suitable for navigation control design. Automated conversion methods for nonlinear models can result in complex LPV representation, which are not suitable for controller synthesis. We apply several state-of-the-art techniques, including learning based approaches, to optimize the complexity and conservatism of the LPV embedding for an GPRV. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is optimal. © 2022, CC BY-NC-SA.

关键词： Principal component analysis

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Human-Exoskeleton Interaction Force Estimation Based on Quasi-Direct Drive Actuators

Human-Exoskeleton Interaction Force Estimation Based on Quas...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Julian Seiler Niklas Schêfer Guoping Zhao Bastian Latsch Martin Grimmer Philipp Beckerle Mario Kupnik Measurement and Sensor Technology Group Technical University of Darmstadt Germany School of Mechanical Engineering Southeast University China Lauflabor Locomotion Laboratory Technical University of Darmstadt Germany Control and Cyber-Physical Systems Technical University of Darmstadt Germany Chair of Autonomous Systems and Mechatronics Friedrich-Alexander-Universität Erlangen Nurnberg Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Exoskeletons have emerged as a promising tech-nological solution to provide functional compensation, training support, physical enhancement, and rehabilitation for an aging population. High torque density and control bandwidth are essential for exoskeleton actuation. In addition, monitoring and control of human-exoskeleton interaction forces is essential for the effectiveness, safety, and comfort of exoskeletons. Quasi-direct drives (QDD) have the potential to address the actuation requirements by enabling proprioceptive actuation with low mechanical output impedance. We present a test bench system to identify and validate the system dynamics, including torque constant, inertia, and friction properties of the QDD Cube-Mars AK10-9 V1.1. The actuator is employed in a hip exoskeleton to evaluate open-loop torque tracking and interaction force estimation in an assisted gait scenario with one participant walking on a treadmill. The model estimates interaction forces with a mean absolute error (MAE) of 2.78±0.58N (6.4% of rated force output). Model-based open-loop control improves the torque tracking MAE by 23%. Our analysis indicates that considering the soft coupling between human and exoskeleton has the potential to further improve the torque tracking and interaction force estimation accuracy. Altogether, the findings demonstrate that QDD actuation enables backdrivability, re-liable force estimation and controllable assistance, thereby providing an effective solution for exoskeleton actuation.

关键词： Actuators Torque Accuracy Friction Exoskeletons Force Dynamics Estimation Impedance Hip

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Performance Evaluation and Comparative Study of Single and Two Stages Inverters Used in Grid-Connected Photovoltaic systems

Performance Evaluation and Comparative Study of Single and T...

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Electrical engineering and Automatic control (ICEEAC), International Conference on

作者： Fayçal Hadj Mihoub Sidi Moussa Abdelkader Morsli Abdelhalim Tlemçani Hocine Belmili Hassan Nouri Electrical Engineering and Automation Research Laboratory (LREA) University of Medea Medea Algeria Solar Equipment Development Unit (UDES) Renewable Energy Development Center (CDER) Bou-Ismail Tipaza Algeria Department of Engineering Design and Mathematics Power Systems Electronics and Control Research Group Bristol U.K

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

This comparative study between two topologies of three-phase grid-connected photovoltaic inverters, namely the single-stage inverter and the two-stage inverter, was modeled and simulated using Matlab Simulink. The control of the two-level inverter is by the synchronous reference frame method, and the tracker of the PV array power is by the incremental conductance MPPT algorithm. In this paper, performance analysis of PV grid-connected inverters based on the overall efficiency and harmonic distortion. The simulation results indicate satisfaction with the quality of the current and voltage inj ected into the electrical grid, where THD is according to the recommendations of the IEEE-519 standard, and both topologies are efficient, but the single-stage inverter performs better than the two-stage inverter.

关键词： Photovoltaic systems Total harmonic distortion Software packages Inverters Mathematical models Topology Voltage control

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DEFT - A Scaled Electric Vehicle Platform for Testing Autonomous Driving Algorithms

TechRxiv

引用

TechRxiv 2024年

作者： Kumar, Subhadeep Pasumarthy, Ramkrishna Bhatt, Nirav P. Department of Electrical Engineering Indian Institute of Technology Madras Chennai600036 India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai600036 India Department of Biotechnology Robert Bosch Center of Data Sciences and Artificial Intelligence Initiative for Biological Systems Engineering Indian Institute of Technology Madras Chennai600036 India

Scaled-down vehicles provide a controlled and repeatable environment for testing autonomous driving algorithms, expediting development while mitigating risks associated with full-scale vehicle testing. This paper introduces a meticulously designed scaled-down electric vehicle, emphasizing vehicle state measurement, precise control, perception sensing, and electric safety. Our approach utilizes actuators with feedback, provides enhanced reliability and precision, and torque across all speed ranges, in contrast to conventional scaled platforms built with actuators of radio-controlled cars. Apart from the conventional sensors used in the existing scaled platforms, our vehicle incorporates novel sensor modules to measure wheel angular velocities, steering angles, and battery cell voltages, which can also be seamlessly integrated into these platforms. We demonstrate the vehicle’s motion with manual and autonomous operation and showcase its features for validation. © 2024, CC BY.

关键词： Automobile testing

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Towards explainable data-driven predictive control with regularizations

arXiv

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arXiv 2025年

作者： Klädtke, Manuel Darup, Moritz Schulze Control and Cyberphysical Systems Group Faculty of Mechanical Engineering TU Dortmund University Germany

Data-driven predictive control (DPC), using linear combinations of recorded trajectory data, has recently emerged as a popular alternative to traditional model predictive control (MPC). Without an explicitly enforced prediction model, the effects of commonly used regularization terms—and the resulting predictions—can be opaque. This opacity may lead to practical challenges, such as reliance on empirical tuning of regularization parameters based on closed-loop performance, and potentially misleading heuristic interpretations of norm-based regularizations. However, by examining the structure of the underlying optimal control problem (OCP), more precise and insightful interpretations of regularization effects can be derived. In this paper, we demonstrate how to analyze the predictive behavior of DPC through implicit predictors and the trajectory-specific effects of quadratic regularization. We further extend these results to cover typical DPC modifications, including DPC for affine systems, offset regularizations, slack variables, and terminal constraints. Additionally, we provide a simple but general result on (recursive) feasibility in DPC. This work aims to enhance the explainability and reliability of DPC by providing a deeper understanding of these regularization mechanisms. © 2025, CC BY-NC-ND.

关键词： Predictive control systems

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Adaptive control of Discrete-time Nonlinear systems Using ITF-ORVFL

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 556-563页

作者： Xiaofei Zhang Hongbin Ma Wenchao Zuo Man Luo School of Automation Beijing Institute of TechnologyBeijing 100081 School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China School of Automation Beijing Institute of Technologyand also with the State Key Laboratory of Intelligent Control and Decision of Complex Systems(Beijing Institute of Technology)Beijing 100081China School of Automation Beijing Institute of TechnologyBeijing 100081and he is with Beijing Institute of Electronic System EngineeringBeijing 100854China School of Automation Beijing Institute of TechnologyBeijing 100081and she is with Ant GroupBeijing 310013China

Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly *** network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural *** this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training *** link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is *** experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.

关键词： Adaptive control initial-training-free online learning algorithm random vector functional link networks

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