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The Research on Sensorless Control of Permanent Magnet Synchronous using SMC and SMO

IOP Conference Series: Materials Science and Engineering 2020年第1期717卷

作者： Li Daoqi Xiong Xinhong Zhang Guoqing Zhang Yanhui Zuo Rui Feng Wei School of Logistics Engineering Wuhan University of Technology Wuhan 430070 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055 China

This work proposes a way to achieve the sensorless control of permanent-magnet synchronous motor using sliding mode controller (SMC) and Sliding Mode Observer(SMO). In this Paper, the SMC is designed to replace the speed control loop PI controller from the original permanent magnet synchronous motor control system with the sliding mode variable structure control theory. Combined with the modified SMO, the 'SMC and modified SMO' motor control system is proposed. Compared with the 'PI and modified SMO' mode, the performance of the motor control is increased substantially, the interference torque speed attenuation is decreased by 87.5%, and the speed control error was also reduced by 67.7%. There is no overshoot with the reaction speed, and significant improvement in response speed, anti-interference performance, control accuracy and observation precision are achieved. Overall, both the system control and observation performance are significantly improved.

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Design of PI Controller for PMSM using Chaos Particle Swarm Optimization Algorithm

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IOP Conference Series: Materials Science and Engineering 2020年第1期717卷

作者： Zuo Rui Xiong Xinhong Chen Lianbo Guo Shifeng Zhang Yanhui Li Daoqi Feng Wei School of Logistics Engineering Wuhan University of Technology Wuhan 430070 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055 China

The proportional-integral-derivative (PID) controller parameters tuning is critical for improving the performance of permanent magnet synchronous motor (PMSM) systems. To improve the performance of particle swarm optimization (PSO) in the PID parameters tuning, the chaos particle swarm optimization (CPSO) is adopted to tune PI parameters in this work. The PMSM vector control system is selected as the research object, and its speed loop PI controller using CPSO algorithm is implemented. The optimal PI controller parameters are obtained using the CPSO algorithm with ten iterations. The simulation is conducted using MATLAB/Simulink to evaluate to the PI controller performance. Four performance indicators including overshoot, rise time, peak time and ITAE are used to evaluate the PI controller with CPSO, and the results suggest that the CPSO algorithm has better performance and stability compared with other tuning methods.

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Decoding Phases of Matter by Machine-Learning Raman Spectroscopy

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Physical Review Applied 2019年第5期12卷 054049-054049页

作者： Anyang Cui (崔安阳) Kai Jiang (姜凯) Minhong Jiang (江民红) Liyan Shang (商丽燕) Liangqing Zhu (朱亮清) Zhigao Hu (胡志高) Guisheng Xu (许桂生) Junhao Chu (褚君浩) Technical Center for Multifunctional Magneto-Optical Spectroscopy (Shanghai) Department of Materials School of Physics and Electronic Science East China Normal University 200241 Shanghai China Guangxi Key Laboratory of Information Materials Department of Materials Science and Engineering Guilin University of Electronic Technology Guilin 541004 Guangxi China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan 030006 Shanxi China Shanghai Institute of Intelligent Electronics & Systems Fudan University 200433 Shanghai China Key Laboratory of Transparent Opto-Functional Advanced Inorganic Materials Shanghai Institute of Ceramics Chinese Academy of Sciences 201899 Shanghai China

Phase transitions of condensed matter have long been a spotlight issue studied by extensive theoretical and experimental investigations. Machine learning can build an integral model-dominant workflow to statistically analyze the collective dynamics of materials and deduce the structure. We use a support-vector-machine algorithm to propose an effective method to recognize the orthorhombic, tetragonal, and cubic phases as well as to construct the phase diagram in ferroelectric crystals by mining and learning the behavioral vectors of the phonon vibrations in a crystalline lattice from Raman scattering, which is a tool typically used to detect structural properties at the molecular level. This study creates a unifying framework including material synthesis and characterization, feature engineering and principal-component analysis, learner evaluation and optimization, structure prediction, and future development of the model. It paves the way to the application of a generic approach for predicting unexplored structures and materials in the future.

关键词： Lattice dynamics Phase transitions Phonons Ferroelectrics Single crystal materials Machine learning Raman spectroscopy

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Corrigendum to "Do theta oscillations explain the somatosensory change detection mechanism?" Biol. Psychol. 143 (2019) April 103-112

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Biological psychology 2021年 159卷 107995页

作者： Zhilin Zhang Guoxin Guo Jian Zhang Chunlin Li Qiang Huang Ritsu Go Hidenao Fukuyama Shintaro Funahashi Tianyi Yan Jinglong Wu Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Psychiatry Graduate School of Medicine Kyoto University Kyoto Japan. School of Life Science Beijing Institute of Technology Beijing China. Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China. School of Biomedical Engineering Capital Medical University Beijing China. International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China. Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China. Research and Educational Unit of Leaders for Integrated Medical System Center for the Promotion of Interdisciplinary Education and Research Kyoto University Kyoto Japan. Advanced Research Institute for Multidisciplinary Science Beijing Institute of Technology Beijing China. School of Life Science Beijing Institute of Technology Beijing China. Electronic address: yantianyi@***.

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Parallel Driving in CPSS:A Unified Approach for Transport Automation and Vehicle Intelligence

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 577-587页

作者： Fei-Yue Wang Nan-Ning Zheng Dongpu Cao Clara Marina Martinez Li Li Teng Liu IEEE State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Advanced Vehicle Engineering Center Cranfield University Department of Automation Tsinghua University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Qingdao Huituo Intelligent Machine Company

The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems(CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space,considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon(iHorizon)and its applications are also presented towards parallel *** proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.

关键词： ACP theory connected automated driving cyber-physical-social systems(CPSS) iHorizon parallel driving parallel horizon parallel learning parallel reinforcement learning parallel testing

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Flexible disaster response of tomorrow final presentation and evaluation of the CENTAURO system

arXiv

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arXiv 2019年

作者： Klamt, Tobias Rodriguez, Diego Baccelliere, Lorenzo Chen, Xi Chiaradia, Domenico Cichon, Torben Gabardi, Maßimiliano Guria, Paolo Holmquist, Karl Kamedula, Malgorzata Karaoguz, Hakan Kashiri, Navvab Laurenzi, Arturo Lenz, Christian Leonardis, Daniele Hoffman, Enrico Mingo Muratore, Luca Pavlichenko, Dmytro Porcini, Francesco Ren, Zeyu Schilling, Fabian Schwarz, Max Solazzi, Maßimiliano Felsberg, Michael Frisoli, Antonio Gustmann, Michael Jensfelt, Patric Nordberg, Klas Roßmann, Jürgen Süß, Uwe Tsagarakis, Nikos G. Behnke, Sven Autonomous Intelligent Systems University of Bonn Germany Humanoids and Human-Centred Mechatronics Istituto Italiano di Tecnologia Genoa Italy Department of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden PERCRO Laboratory TeCIP Institute Sant'Anna School of Advanced Studies Pisa Italy Institute for Man-Machine Interaction RWTH Aachen University Germany Computer Vision Laboratory Linköping University Sweden Lausanne Switzerland Kerntechnische Hilfsdienst GmbH Karlsruhe Germany School of Electrical and Electronic Engineering University of Manchester United Kingdom

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Wheeled Mobile Robot Based on Adaptive Linear Quadratic Gaussian Control 29

Wheeled Mobile Robot Based on Adaptive Linear Quadratic Gaus...

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第29届中国控制与决策会议

作者： Yubin Liao Yongsheng Ou Shan Meng Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Shenzhen University

ISBN: (纸本)9781509046584

To deal with the existing issues of input saturations and uncertain positioning corrupted by Gaussian noises in the tracking control of Wheeled Mobile Robot(WMR),this paper presents a framework which applies the Adaptive Linear Quadratic Gaussian(ALQG) control with Genetic Algorithm based on linear discrete *** work of ALQG includes three ***,to address the problem of input saturations,we consider a linear discrete system as the Lagrangian multiplier and saturation constraint as the inequality constraint,and the linear quadratic controller with the penalty function as the objective function is solved by the interior point ***,in the above implementation of Linear Quadratic Regulator control,the weighting matrix under different conditions may result in different tracking *** address this issue,weighting matrices of different initial poses and reference trajectories are adaptively explored by Genetic Algorithm with control performance indexes as constraint functions,and the optimal weighting matrix is final ***,concerning the uncertainty problem in positioning,the Kalman Filter algorithm with real-time measurement of the disturbed position of the posture filter is applied in real time to obtain more accurate *** ALQG simulation of the WMR is carried out in the MATLAB software platform,and the tracking quality of the ALQG is discussed *** results show that the trajectory tracking controller of wheeled mobile robot based on ALQG can track the target trajectory stably and has good anti-interference abilities.

关键词： Mobile Robot Trajectory Tracking Linear Quadratic Gaussian Control

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Multi-pose moving human detection from a patrol robot in real-time 49

Multi-pose moving human detection from a patrol robot in rea...

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49th International Symposium on robotics, ISR 2017

作者： Wu, Qingtian Guo, Huiwen Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Acad. of Sci. Guangdong Provincial Key Laboratory of Robotics and Intelligent System China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences China

In this paper, a multi-pose human detection system is presented for a patrol robot in real-time. 20 fps (frame per second) and a satisfying detection accuracy of multi-pose human detection in complex outdoor environments can be achieved in the system with no additional special hardware like GPU (graphics processing unit). First, 30,000 positive samples with various appearances are selected manually from different camera angles and are used to train the convolutional neural network (CNN) classifier. Secondly, optical flow of two successive frames taken by on-board camera is used to extract region of interests (ROIs) and a series of post-processing methods, i.e., average filtering, morphological operation and extraction of outer contour are applied for the real-time requirement. Thirdly, a two-stream CNN combining appearance and motion information are applied to reduce the false detection accuracy. Field experiments in complex outdoor environments have been performed to verify that the proposed system can be used to detect multi-pose humans with high detection accuracy in realtime. © 49th International Symposium on robotics, ISR 2017. All rights reserved.

关键词： Graphics processing unit

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Distributed Leader-follower Formation Control of Nonholonomic Mobile Robots

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IFAC-PapersOnLine 2019年第15期52卷 67-72页

作者： Qun Lu Zhiqiang Miao Dan Zhang Li Yu Wenjun Ye Simon X. Yang Chun-Yi Su College of Electrical Engineering Yancheng Institute of Technology Yancheng 224003 China College of Electrical and Information Engineering Hunan University Changsha 410082 China College of Information Engineering Zhejiang University of Technology Hangzhou 310023 China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal QC H3G 1M8 Canada Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph Guelph ON N1G 2W1 Canada School of Automation Guangdong University of Technology Guangzhou 510006 China on leave from Concordia University Montreal QC H3G 1M8 Canada

This paper considers the problem of distributed leader-follower formation control for nonholonomic mobile robots only using local interactions among the robots. First, a distributed estimation strategy is presented for each follower robot to estimate the leader’s state. Then the dynamic equations of the formation tracking errors are formulated and a distributed formation control scheme is designed, which yields globally uniformly ultimately bounded stability for the closed-loop systems without requiring persistent excitation conditions. Finally, a simulation example demonstrates the effectiveness of the proposed approach.

关键词： Distributed formation control nonholonomic mobile robots distributed formation control persistent exciting conditions

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Research on Position Sensorless Control of PMSM Based on Improved SMO

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IOP Conference Series: Materials Science and Engineering 2019年第1期533卷

作者： Zhang Guoqing Xiong Xinhong Zheng Yousheng Zhang Yanhui Chen Lianbo Wu Xinyu Feng Wei Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055 China School of Logistics Engineering Wuhan University of Technology Wuhan 430070 China

The sliding mode observer (SMO) can effectively achieve the permanent magnet synchronous motor (PMSM) to perform position sensorless control, but there are still drawbacks of system chattering and high complexity. In this paper, an improved SMO is established based on hyperbolic tangent function and Kalman filter. The system chattering is effectively suppressed, and the accuracy of position and speed observation error is significantly improved without phase compensation. It is found that the convergence rate of the hyperbolic tangent function has a strong correlation with the system chattering degree and the speed observation error. When a is between 3 and 4, the speed error accuracy of the improved SMO is the optimum situation.

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