检索结果-内蒙古大学图书馆

Active Disturbance Rejection control of High-Order Flat Underactuated Systems: Mass-Spring Benchmark Problem 20th

学校读者我要写书评

暂无评论

Active Disturbance Rejection Control of High-Order Flat Unde...

20th Polish control Conference, PCC 2020

作者： Madonski, Rafal Ramirez-Neria, Mario Giernacki, Wojciech Energy Electricity Research Center International Energy College Jinan University ZhuhaiGuangdong Province519070 China Mechatronics Department Universidad Politecnica del Valle de Mexico UPVM Tultitlan de Mariano Escobedo Fuentes del Valle54910 Mexico Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznan60-965 Poland

ISBN: (纸本)9783030509354

A custom active disturbance rejection control (ADRC) is proposed for trajectory tracking in uncertain underactuated systems. By utilizing a particular property of the differentially flat plant model, a robust output-feedback controller with a novel cascade of extended state observers (ESO) is introduced. It effectively deals with the problem of governing high-order plants without over-amplification of the measurement noise, typically seen in conventional single high-gain observer-centered control approaches. The proposed solution is based on full utilization of the information already available about the governed system, without necessity for additional measurement devices. In order to be easily implementable, it assumes only limited knowledge of the system model and is expressed in an industry familiar error-based form with a straightforward tuning method. A representable two-mass three-spring benchmark problem is used throughout the paper to convey the proposed idea and evaluate it experimentally on a laboratory testbed. © 2020, Springer Nature Switzerland AG.

关键词： Disturbance rejection

Minimization of constraint violation probability in model predictive control

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Brüdigam, Tim Gaßmann, Victor Wollherr, Dirk Leibold, Marion The Department of Automatic Control Engineering The Technical University of Munich Germany The Department of Robotics Artificial Intelligence and Real-time Systems The Technical University of Munich Germany

While Robust Model Predictive control considers the worst-case system uncertainty, Stochastic Model Predictive control, using chance constraints, provides less conservative solutions by allowing a certain constraint violation probability depending on a predefined risk parameter. However, for safety-critical systems it is not only important to bound the constraint violation probability but to reduce this probability as much as possible. Therefore, an approach is necessary that minimizes the constraint violation probability while ensuring that the Model Predictive control optimization problem remains feasible. We propose a novel Model Predictive control scheme that yields a solution with minimal constraint violation probability for a norm constraint in an environment with uncertainty. After minimal constraint violation is guaranteed the solution is then also optimized with respect to other control objectives. Further, it is possible to account for changes over time of the support of the uncertainty. We first present a general method and then provide an approach for uncertainties with symmetric, unimodal probability density function. Recursive feasibility and convergence of the method are proved. A simulation example demonstrates the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Robust control

Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

学校读者我要写书评

暂无评论

IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

Learning control barrier functions from expert demonstrations

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Robey, Alexander Hu, Haimin Lindemann, Lars Zhang, Hanwen Dimarogonas, Dimos V. Tu, Stephen Matni, Nikolai Department of Electrical and Systems Engineering University of Pennsylvania Division of Decision and Control Systems KTH Royal Institute of Technology Google Brain Robotics

Inspired by the success of imitation and inverse reinforcement learning in replicating expert behavior through optimal control, we propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs). We consider the setting of a known nonlinear control affine dynamical system and assume that we have access to safe trajectories generated by an expert a practical example of such a setting would be a kinematic model of a self-driving vehicle with safe trajectories (e.g., trajectories that avoid collisions with obstacles in the environment) generated by a human driver. We then propose and analyze an optimization-based approach to learning a CBF that enjoys provable safety guarantees under suitable Lipschitz smoothness assumptions on the underlying dynamical system. A strength of our approach is that it is agnostic to the parameterization used to represent the CBF, assuming only that the Lipschitz constant of such functions can be efficiently bounded. Furthermore, if the CBF parameterization is convex, then under mild assumptions, so is our learning process. We end with extensive numerical evaluations of our results on both planar and realistic examples, using both random feature and deep neural network parameterizations of the CBF. To the best of our knowledge, these are the first results that learn provably safe control barrier functions from data. Copyright © 2020, The Authors. All rights reserved.

关键词： Parameterization

Urban Digital Twins for Intelligent Road Inspection

学校读者我要写书评

暂无评论

Urban Digital Twins for Intelligent Road Inspection

IEEE International Conference on Big Data

作者： Rui Fan Yikang Zhang Sicen Guo Jiahang Li Yi Feng Shuai Su Yanting Zhang Wenshuo Wang Yu Jiang Mohammud Junaid Bocus Xingyi Zhu Qijun Chen the Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems and State Key Laboratory of Intelligent Autonomous Systems Robotics & Artificial Intelligence Laboratory (RAIL) Tongji University Shanghai P. R. China Department of Computer Science & Technology Donghua University Shanghai P. R. China Department of Civil Engineering McGill University Montreal Canada CTO office at ClearMotion Billerica MA USA Department of Electrical & Electronic Engineering the University of Bristol BS8 1UB U.K Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University Shanghai P. R. China

ISBN: (纸本)9781665480468

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022.

关键词： Roads Data acquisition Inspection Big Data Rough surfaces Digital twins

Artificial Neural Network-Based Direct Power control to Enhance the Performance of a Pmsg-Wind Energy Conversion System Under Real Wind Speed and Parameter Uncertainties: An Experimental Validation

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Majout, Btissam Bossoufi, Badre Karim, Mohammed Skruch, Pawel Mobayen, Saleh El Mourabit, Youness El Zair Laggoun, Zakaria Laboratory of Engineering Modelling and Systems Analysis SMBA University Fez Morocco Department of Automatic Control and Robotics AGH University of Science and Technology Kraków30-059 Poland Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Laboratory of Electrotechnique Constantine Mentouri Brothers Constantine 1 University Constantine Algeria

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power control (DPC) approach has garnered significant attention, owing to its straightforward and highly adaptable algorithm. It has been adopted in many applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system’s performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board. © 2023, The Authors. All rights reserved.

关键词： MATLAB

Modular smart furniture system for independent living of older adults- user experience study

学校读者我要写书评

暂无评论

Gerontechnology 2020年第4期19卷 1-13页

作者： Merilampi, Sari Poberžnik, Anja Saari, Santeri Serrano, J. Artur Güttler, Jörg Langosch, Katharina Bock, Thomas Zou, Ling Magne, Trine A. Faculty of Technology Satakunta University of Applied Sciences Pori Finland Department of Neuromedicine and Movement Science Faculty of Medicine and Health Sciences NTNU/Norwegian University of Science and Technology Trondheim Norway Chair of Building Realization and Robotics Technical University of Munich Munich Germany School of Microelectronics and Control Engineering Changzhou University Changzhou 213164 China

Background: The ageing of the population represents a significant challenge, however, it is also a huge potential driver for innovation. Technology development capabilities are rapidly increasing, which enables new, innovative solutions when considering the ageing population. Objective: To promote the successful development and implementation of new technologies (in this case, smart furniture), positive user feedback and experiences are required. Methods: In this study, several modular smart furniture prototypes were developed, offering a potential solution to several challenges associated with ageing. The aim was to investigate which features positively affect user experience and consequently form the basis for smart, ageing-friendly furniture design guidelines. The smart furniture systems consisted of five prototypes: Fall Detection system with assistive Mobile Robot, ReAbleChair, Magic Mirror, Smart Gaming Chair, and 3D printed handles. These prototypes were presented to seniors in a home-like environment, after which participants completed a questionnaire and were interviewed. The themes used in the semi-structured interview followed the structure of Jesse Garrett's user experience framework. Results: The most essential feature was found to be modularity, or the ability to customize solutions according to the end user's capabilities and needs. In addition, results highlight that smart furniture should be aesthetically pleasing whilst still serving its primary purpose (i.e. as an item of furniture). Administrative components of the software should be removed from the end user's User Interface (UI) and integrated in a separate UI, to simplify the end user's interaction with the technology. Installation and customization services, as well as value-adding services (i.e. monitoring) were seen to offer potential. Conclusions: In general, the user experiences were positive. Smart features were readily accepted as part of the furniture prototypes, with hidden technologi

关键词： active and assistive living assistive technologies design for older adults fall detection serious games Smart furniture

Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Dhédin, Victor Li, Haolong Khorshidi, Shahram Mack, Lukas Ravi, Adithya Kumar Chinnakkonda Meduri, Avadesh Shah, Paarth Grimminger, Felix Righetti, Ludovic Khadiv, Majid Stueckler, Joerg 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 United States Oxford Robotics Institute University of Oxford United Kingdom 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. Copyright © 2022, The Authors. All rights reserved.

关键词： Extended Kalman filters

NccFlow: Unsupervised learning of optical flow with non-occlusion from geometry

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wang, Guangming Ren, Shuaiqi Wang, Hesheng The Key Laboratory of System Control and Information Processing of Ministry of Education Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optical flow. First, neighboring pixels in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixels in the first frame moves to the second frame, no other pixels will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric laws in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer vision