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A Nonlinear MPC-Net Optimization Framework for Wheeled Humanoid Robots with Whole Body Dynamics

IEEE Transactions on Automation Science and engineering 2025年

作者： Li, Guoxin Liu, Xingjian Wu, Xiaoyu Tang, Qirong Zhang, Hao Li, Zhijun Shi, Peng Tongji University School of Mechanical Engineering Translational Research Center Shanghai YangZhi Rehabilitation Hospital Shanghai Sunshine Rehabilitation Center Shanghai200092 China Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Shanghai201804 China State Key Laboratory of High-Performance Precision Manufacturing Dalian116024 China Tongji University School of Mechanical Engineering Shanghai201804 China National University of Singapore Department of Biomedical Engineering 117583 Singapore Tongji University Department of Control Science and Engineering Shanghai201804 China The University of Adelaide School of Electrical and Mechanical Engineering SA5005 Australia Obuda University Budapest1034 Hungary

The task performance of mobile manipulators can be significantly enhanced by whole-body control and optimization in complex scenarios. Due to the nonlinear properties of whole-body dynamics and parameter uncertainty, modeling accurate system dynamics is essential in addition to designing an effective control strategy. However, traditional control methods have high computational costs and fail to deal with the parameter errors caused by model linearization. To address these issues, we propose a model predictive control (MPC)-Net, a learning-based approach that facilitates rapid online optimization by combining deep learning with multiple MPCs. Firstly, we develop a parameter identification algorithm based on a deep neural model to estimate the unknown dynamics parameters. Although the control performance of the MPC approach positively correlated with the prediction horizon, a long horizon would result in additional computational costs. Thus, MPC-Net is constructed by combining multiple sub-MPC issues, and the nonlinear coefficients are obtained by using a deep neural network-based optimization framework. Furthermore, MPC-Net generates the solution by combining the outputs of multiple sub-MPC problems using the nonlinear transformation of learned coefficients. Experiments are conducted on a mobile manipulator, which demonstrates the proposed MPC-Net-based optimization control offers fast efficient computation and low tracking error performance. © 2004-2012 IEEE.

关键词： Predictive control systems

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Reviewing Fault Diagnosis Methods in Electric Drives: Power Subsystem and Electrical Machine

Reviewing Fault Diagnosis Methods in Electric Drives: Power ...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Galina Demidova Hao Chen Yibin Li Dmitry Lukichev Alecksey Anuchin Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China Institute of Marine Science and Technology Shandong University Shandong China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

This paper provides a review of popular fault diagnosis methods in the power subsystem and electrical machine of electric drives. The review aims to explore the different approaches used to detect faults in these components and the most common types of faults encountered in electric drives. The paper first provides an overview of the power subsystem and electrical machine and their respective functions within the electric drive system. It then proceeds to discuss the different types of faults that can occur in these components, including overvoltage, undervoltage, overcurrent, and short circuits. The review evaluates the various fault diagnosis methods available for these components, including model-based, signal-based, and artificial intelligence-based approaches. Model-based approaches use mathematical models of the system to detect and diagnose faults. Signal-based approaches rely on the analysis of measured signals to detect changes in system behavior that indicate a fault. Artificial intelligence-based approaches use machine learning algorithms to identify patterns in data and detect faults. Model-based approaches are highly accurate but can be computationally intensive. Signal-based approaches are less computationally intensive but may be less accurate in certain situations. Artificial intelligence-based approaches are versatile but require significant amounts of data for training. This review provides a comprehensive overview of the current state-of-the-art in fault diagnosis methods for the power subsystem and electrical machine of electric drives. The review concludes by suggesting potential areas for future research to further improve the accuracy and efficiency of fault diagnosis methods for electric drives.

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Output Robust Tracking control of Permanent Magnet Synchronous Motors ⁎

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IFAC-PapersOnLine 2021年第14期54卷 197-202页

作者： Anton Pyrkin Alberto Isidori Oleg Borisov Faculty of Control Systems and Robotics ITMO University St. Petersburg Russia Department of Computer Control and Management Engineering “A. Ruberti” University of Rome “La Sapienza” Rome Italy

This paper shows the design of output robust tracking control by means of the enhanced extended observer and the internal model with application to permanent magnet synchronous motors. Two types of references, namely a biased sinusoidal signal and a “ramp”, along with a constant disturbance represented by the load torque have been addressed. The performance achieved by this control framework is shown by simulations.

关键词： Output regulation Tracking control Robust control Internal model PMSM

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Admittance control Based Gait Intervention Strategy of Knee Exoskeleton for Stance Phase Support

Admittance Control Based Gait Intervention Strategy of Knee ...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Xiaolin Dai Zhihao Zhou Zilu Wang Xuewen Rong Qining Wang School of Control Science and Engineering Shandong University Jinan China University of Health and Rehabilitation Sciences Qingdao China Institute for Artificial Intelligence Peking University Beijing China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering Beijing China College of Engineering Peking University Beijing China School of Control Science and Engineering Shandong University and Engineering Research Center of Intelligent Un-manned System of Ministry of Education Jinan China Department of Advanced Manufacturing and Robotics College of Engineering with the Institute for Artificial Intelligence Peking University Beijing China Peking University Third Hospital Beijing China Fifth Affiliated Hospital of Zhengzhou University Henan China

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Knee exoskeletons have been utilized for gait rehabilitation in patients after total knee arthroplasty. During the early and middle stance phases of walking gaits, the knee joint activity of these patients is either absent or reduced, which is not conducive to the rehabilitation of normal gaits. Currently, most exoskeletons used for the rehabilitation of patients after TKA correct gait by pre-setting the movement trajectory of the knee joint. However, this method faces challenges in terms of human-machine interaction and safety. This paper proposes a strategy that combines admittance control during the early and middle stance phases with position control during the late stance and swing phases to improve compliance and safety between the wearer and the knee exoskeleton. The exoskeleton can provide support to the knee joint during the early and middle stance phases according to the wearer’s actual movement requirements. The performance and safety of the admittance control were verified through test bench and squat-to-stand experiments, while the gait intervention strategy was validated through a downhill walking experiment.

关键词： Knee Legged locomotion Torque Mechatronics Exoskeletons Position control Safety

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Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

arXiv

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arXiv 2024年

作者： Ou, Ni Chen, Zhuo Luo, Shan The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China The Robot Perception Lab. Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are available at the project website https://***/Marker-Markerless-Transition/ Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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Annual Conference of Industrial Electronics Society

作者： Haibo Cheng Yunpeng He Peng Zeng Shichao Li Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

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An Improved Method for Small Target Recognition Based on Faster RCNN 17th

An Improved Method for Small Target Recognition Based on Fas...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Liu, Qun-po Wang, Qi-jing Hanajima, Naohiko Su, Bo Department of Robotics Engineering Henan Polytechnic University 2001 Century Avenue Jiaozuo Henan454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment 2001 Century Avenue Jiaozuo Henan454003 China College of Information and Systems Muroran Institute of Technology 27-1 Mizumoto-cho Muroran-shi Hokkaido050-8585 Japan

ISBN: (纸本)9789811663239

As one of the most challenging tasks in the field of deep learning, target detection has achieved remarkable results. The existing algorithms have achieved excellent results in the detection of large and medium-sized targets, but the detection accuracy of small targets is not ideal because of their small size and weak features. A small target recognition method based on improved Faster_Rcnn that aims at the problem of low accuracy in small target detection is proposed in this paper. Since the ROI Pooling method in Faster Rcnn might cause quantization errors in the operation process, resulting in inaccurate positioning and other problems during detection, an improved ROI Align method is adopted to eliminate the quantization errors. The cell public data set is used for the experiment, the results show that the average recognition accuracy of the recognition model with improved algorithm is 92.11%, which is an increase of 7%. The method also obtains good effect on NWPU VHR-10 data set, which indicates that it has good universality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Simulation and Design of Receiver Array for Light Carrying Orbital Angular Momentum

Simulation and Design of Receiver Array for Light Carrying O...

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Laser Science, LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： McGavick, Marco Avramov-Zamurovic, Svetlana United States Naval Academy. Echo Company The Basic School 24164 Belleau Avenue QuanticoVA22134 United States Weapons Robotics and Control Engineering Department United States Naval Academy MD21412 United States

Received light intensity is captured by a sensor array that generates images for machine learning recognition software. Results indicate that a binary 12x12 sensor array could decode light carrying orbital angular mom... 详细信息

ISBN: (纸本)9781557528209

关键词： Angular momentum

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Platoon-Based Distributed control for Automated Material Handling Systems

Platoon-Based Distributed Control for Automated Material Han...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Alessandro Bozzi Jose-Fernando Jimenez Camilo Hernandez-Rodriguez Eliana-Maria Gonzalez-Neira Damien Trentesaux Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Italy Laboratory System and Materials for Mechatronics (SYMME) Université Savoie Mont Blanc Annecy France Industrial Engineering Department Pontificia Universidad Javeriana Bogota Columbia Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science (LAMIH) Université Polytechnique Hauts-de-France Valenciennes France

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

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Distributed online convex optimization with compressed communication 22

Distributed online convex optimization with compressed commu...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Zhipeng Tu Xi Wang Yiguang Hong Lei Wang Deming Yuan Guodong Shi Academy of Mathematics and Systems Science Chinese Academy of Sciences China and Australian Center for Field Robotics School of AMME The University of Sydney Australia Department of Control Science and Engineering Tongji University China College of Control Science and Engineering Zhejiang University China School of Automation Nanjing University of Science and Technology China Australian Center for Field Robotics School of AMME The University of Sydney Australia

ISBN: (纸本)9781713871088

We consider a distributed online convex optimization problem when streaming data are distributed among computing agents over a connected communication network. Since the data are high-dimensional or the network is large-scale, communication load can be a bottleneck for the efficiency of distributed algorithms. To tackle this bottleneck, we apply the state-of-art data compression scheme to the fundamental GD-based distributed online algorithms. Three algorithms with difference-compressed communication are proposed for full information feedback (DC-DOGD), one-point bandit feedback (DC-DOBD), and two-point bandit feedback (DC-DO2BD), respectively. We obtain regret bounds explicitly in terms of time horizon, compression ratio, decision dimension, agent number, and network parameters. Our algorithms are proved to be no-regret and match the same regret bounds, w.r.t. time horizon, with their uncompressed versions for both convex and strongly convex losses. Numerical experiments are given to validate the theoretical findings and illustrate that the proposed algorithms can effectively reduce the total transmitted bits for distributed online training compared with the uncompressed baseline.

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