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A Method to Track and Acquire the 3D Point Cloud Data of Object 8

A Method to Track and Acquire the 3D Point Cloud Data of Obj...

8th IEEE Annual International Conference on Cyber Technology in Automation, control, and Intelligent systems (IEEE-CYBER)

作者： Wang, Zunran Yang, Chenguang Cong, Yang Li, Zhijun South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538670576

In the paper, a method to track an object and acquire the 3D point cloud data of the object is proposed, because real-time tracking of an object is challenging work. A robust Bayesian framework is designed in this method for visual tracking of human motion. In this paper, a 3D bounding box is exploited to acquire the spatial range of the human body. Besides, the mapping of the coordinate relation between RGB image and point cloud data is realized through the camera calibration. The experiment results demonstrate that the method can offer a faster convergence of estimate. In addition, the prior distributions of the particles at each sample are defined based on the color histograms of the human body.

关键词： Human Body Human motion CLOUDS Clouds Track visual tracking camera calibration PARTICLE DISTRIBUTION

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Fuzzy Tracking control for a Class of Uncertain MIMO Nonlinear systems With State Constraints

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2019年第3期49卷 543-554页

作者： He, Wei Kong, Linghuan Dong, Yiting Yu, Yao Yang, Chenguang Sun, Changyin Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Robot Chengdu 611731 Sichuan Peoples R China Texas Tech Univ Dept Mech Engn Lubbock TX 79409 USA South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China

In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multiple-input and multiple-output (MIMO) nonlinear systems subject to unknown dynamics and state constraints. FNNs are used to approximate the unknown dynamics that comprises the effects of uncertain parameters and functions. Also, integral Lyapunov functions are introduced to address state constraints. A neural-network-based observer is designed to estimate the unmeasurable states. With state-feedback and output feedback tracking control, the stability of closed-loop system is guaranteed via Lyapunov's stability theory. Two cases of simulations for MIMO systems with state constraints are conducted to verify the effectiveness of the proposed control.

关键词： Adaptive control fuzzy control multiple-input and multiple-output (MIMO) nonlinear systems state constraints

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Discrete time-variant nonlinear optimization and system solving via integral-type error function and twice ZND formula with noises suppressed

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SOFT COMPUTING 2018年第21期22卷 7129-7141页

作者： Shi, Yang Zhang, Yunong Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China

In this paper, by using integral-type error function and twice zeroing neural-dynamics (or termed, Zhang neural-dynamics, ZND) formula, continuous-time advanced zeroing neural-dynamics (CT-AZND) model is proposed for solving the continuous time-variant nonlinear optimization problem. Furthermore, a discrete-time advanced zeroing neural-dynamics (DT-AZND) model is first proposed, analyzed, and investigated for solving the discrete time-variant nonlinear optimization (DTVNO) problem. Theoretical analyses show that the proposed DT-AZND model is convergent, and its steady-state residual error has an O(g(3)) pattern with g denoting the sampling gap. In addition, in the presence of various kinds of noises, the proposed DT-AZND model possesses advantaged performance. In detail, the proposed DT-AZND model converges toward the time-variant theoretical solution of the DTVNO problem with O(g(3)) residual error in the presence of an arbitrary constant noise and has excellent ability to suppress linear-form time-variant noise and bounded random noise. Illustrative numerical experiments further substantiate the efficacy and advantage of the proposed DT-AZND model for solving the DTVNO problem.

关键词： Discrete-time advanced zeroing neural-dynamics (DT-AZND) Discrete time-variant nonlinear optimization (DTVNO) Noise suppression Convergence Residual error

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New Discrete-Time ZNN Models for Least-Squares Solution of Dynamic Linear Equation System With Time-Varying Rank-Deficient Coefficient

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2018年第11期29卷 5767-5776页

作者： Qiu, Binbin Zhang, Yunong Yang, Zhi SYSU Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China

In this brief, a new one-step-ahead numerical differentiation rule called six-instant g-cube finite difference (6IgCFD) formula is proposed for the first-order derivative approximation with higher precision than existing finite difference formulas (i.e., Euler and Taylor types). Subsequently, by exploiting the proposed 6IgCFD formula to discretize the continuous-time Zhang neural network model, two new-type discrete-time ZNN (DTZNN) models, namely, new-type DTZNNK and DTZNNU models, are designed and generalized to compute the least-squares solution of dynamic linear equation system with time-varying rank-deficient coefficient in real time, which is quite different from the existing ZNN-related studies on solving continuous-time and discrete-time (dynamic or static) linear equation systems in the context of full-rank coefficients. Specifically, the corresponding dynamic normal equation system, of which the solution exactly corresponds to the least-squares solution of dynamic linear equation system, is elegantly introduced to solve such a rank-deficient least-squares problem efficiently and accurately. Theoretical analyses show that the maximal steady-state residual errors of the two new-type DTZNN models have an O(g(4)) pattern, where g denotes the sampling gap. Comparative numerical experimental results further substantiate the superior computational performance of the new-type DTZNN models to solve the rank-deficient least-squares problem of dynamic linear equation systems.

关键词： Dynamic linear equation system least-squares solution new-type discrete-time Zhang neural network (DTZNN) models six-instant g-cube finite difference (6IgCFD) formula time-varying rank-deficient coefficient

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Personalized Variable Gain control With Tremor Attenuation for Robot Teleoperation

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2018年第10期48卷 1759-1770页

作者： Yang, Chenguang Luo, Jing Pan, Yongping Liu, Zhi Su, Chun-Yi South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Natl Univ Singapore Dept Biomed Engn Singapore 117583 Singapore Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

Teleoperated robot systems are able to support humans to accomplish their tasks in many applications. However, the performance of teleoperation largely depends on motor functionality and human operator's skill, especially when a human operator is short of skill training. In order to adapt to various unstructured environments for the robot system and the human operator, in this paper, a teleoperation scheme using integrated tremor attenuation with a variable gain control algorithm involving surface electromyogram is proposed to achieve personalized control performance and to reduce reliance on operator's skill. For attenuating tremor, a filter based on support vector machine is developed to guarantee normal operation. This filter depends on the machine learning scheme and does not rely on a priori filter parameters. Semiphysical experiments have been performed to demonstrate the effectiveness of the proposed methods.

关键词： Personalized control surface electromyographic (sEMG) teleoperated robot system tremor attenuation variable gain control

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Neural network-based discrete-time Z-type model of high accuracy in noisy environments for solving dynamic system of linear equations

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NEURAL COMPUTING & APPLICATIONS 2018年第11期29卷 1217-1232页

作者： Jin, Long Zhang, Yunong Qiu, Binbin Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China SYSU CMU Shunde Int Joint Res Inst Foshan 528300 Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China

To solve dynamic system of linear equations with square or rectangular system matrices in real time, a discrete-time Z-type model based on neural network is proposed and investigated. It is developed from and studied with the aid of a unified continuous-time Z-type model. Note that the framework of such a unified continuous-time Z-type model is generic and has a wide range of applications, such as robotic redundancy resolution with quadratic programming formulations. To do so, a one-step-ahead numerical differentiation formula and its optimal sampling-gap rule in noisy environments are presented. We compare the Z-type model extensively with E-type and N-type models. Theoretical results on stability and convergence are provided which show that the maximal steady-state residual errors of the Z-type, E-type and N-type models have orders O(tau(3)), O(tau(2)), and O(tau), respectively, where tau is the sampling gap. We also prove that the residual error of any static method that does not exploit the time-derivative information of a time-dependent system of linear equations has order O(tau) when applied to solve discrete real-time dynamic system of linear equations. Finally, several illustrative numerical experiments in noisy environments as well as two application examples to the inverse-kinematics control of redundant manipulators are provided and illustrated. Our analysis substantiates the efficacy of the Z-type model for solving the dynamic system of linear equations in real time.

关键词： Recurrent neural network Discrete-time Z-type model Dynamic system of linear equations Noisy environments Residual error

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Analysis, Verification and Comparison on Feedback-Aided MA Equivalence and Zhang Equivalency of Minimum-Kinetic-Energy Type for Kinematic control of Redundant Robot Manipulators

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ASIAN JOURNAL OF control 2018年第6期20卷 2154-2170页

By following and generalizing Ma et al.'s inspiring work and Zhang et al.'s inspiring work, this paper proposes and investigates three kinds of equivalent relationships with error-feedback information incorporated for the minimum-kinetic-energy (MKE) redundancy resolution. Specifically, these three equivalent relationships are divided into two big classes in terms of scheme formulations: one is termed MKE-type Ma equivalence (MKE-ME);the other is MKE-type Zhang equivalency (MKE-ZE), which can be further subdivided into MKE-ZE-I and MKE-ZE-II. Also, the equivalent analyses as well as the primary distinctions about three such equivalent relationships are presented. The simulation results based on a PUMA560 robot manipulator and a three-link planar robot manipulator not only verify the efficacy of the corresponding velocity-level and acceleration-level schemes of MKE-ME, MKE-ZE-I and MKE-ZE-II, but also substantiate their reasonableness and effectiveness of these three equivalent relationships. More importantly, the comparative numerical results show their respective characteristics and advantages for kinematic control of redundant robot manipulators.

关键词： Minimum kinetic energy (MKE) MKE-type Ma equivalence MKE-type Zhang equivalency feedback-aided redundant robot manipulators kinematic control

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Multi-Object Selective Assembly Based on Hybrid Particle Swarm Algorithm

Multi-Object Selective Assembly Based on Hybrid Particle Swa...

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第37届中国控制会议

作者： Liu Weidong Liu Haiming Guo Jinhui Key laboratory of autonomous systems and networked control Ministry of education South China university of technology

Considering the batch assembly with multiple function requirements（FR）, the multi-object selective assembly（SA） technology based on the hybrid particle swarm algorithm is introduced in this paper. Firstly, the SA model is discussed, and a multi-object optimization problem is illustrated based on the assembly chains;additionally, the hybrid particle swarm algorithm is used to solve the multi-object selective assembly problem. The matching matrix of batch parts is designed as the particle. The local non-inferior particles and the global non-inferior particles of each iteration population are calculated. The particle population is update by crossover and mutation operations. After all the iterations, the final global non-inferior particles are selected as the matching strategy of batch parts. Based on the optimized matching strategy, the assembly success rate is calculated, and each assembly accuracy and its precision is guaranteed. The process of multi-object SA based on hybrid particle swarm algorithm is introduced, and an example of batch cabin sections are illustrated to obtain the optimized matching strategy of batch parts. Contrasted to the sequential strategy and the random strategy, it can find that the multi-object SA method based on hybrid particle swarm algorithm has advantage in guaranteeing the assembly success rate, and comprehensively decreasing each assembly accuracy.

关键词： multi-object selective assembly hybrid particle swarm algorithm assembly chain non-inferior particles matching strategy of batch parts

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Optimal Feature Selection for EMG-Based Finger Force Estimation Using LightGBM Model

Optimal Feature Selection for EMG-Based Finger Force Estimat...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yuhang Ye Chao Liu Nabil Zemiti Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Robotics LIRMM CNRS-University of Montpellier Montpellier France Bristol Robotics Laboratory University of the West of England Bristol UK

Electromyogram (EMG) signal has been long used in human-robot interface in literature, especially in the area of rehabilitation. Recent rapid development in artificial intelligence (AI) has provided powerful machine learning tools to better explore the rich information embedded in EMG signals. For our specific application task in this work, i.e. estimate human finger force based on EMG signal, a LightGBM (Gradient Boosting Machine) model has been used. The main contribution of this study is the development of an objective and automatic optimal feature selection algorithm that can minimize the number of features used in the LightGBM model in order to simplify implementation complexity, reduce computation burden and maintain comparable estimation performance to the one with full features. The performance of the LightGBM model with selected optimal features is compared with 4 other popular machine learning models based on a dataset including 45 subjects in order to show the effectiveness of the developed feature selection method.

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A new information-weighted recursive algorithm for time-varying systems: application to UAV system identification

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INTERNATIONAL JOURNAL OF systems SCIENCE 2018年第11期49卷 2477-2489页

作者： Liu, Zun Ji, Honghai Pei, Hailong Lewis, Frank L. South China Univ Technol Key Lab Autonomous Syst & Networked Control Minist Educ Unmanned Aerial Vehicle Syst Engn Technol Res Ctr Guangzhou Guangdong Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing Peoples R China Univ Texas Arlington UTA Res Inst Ft Worth TX USA North China Univ Technol Coll Elect & Control Engn Beijing Peoples R China

This paper presents a new recursive identification method which can efficiently estimate time-varying parameters in discrete time systems and has significant advantages over standard recursive least-squares (RLS) method. This new information-weighted recursive algorithm for time-varying systems has three novel features, discounting of inaccurate estimates through weighting by the Information matrix, using the reuse of past data in computing current parameter estimates, a new tuneable damping factor parameter and a precisely designed compensation term to neutralise the estimation error caused by time-varying coefficients. A rigorous proof of convergence is also provided. Simulations show that the new algorithm significantly outperforms standard RLS, exhibiting better tracking performance and faster convergence. Flight tests on a T-REX 800 helicopter Unmanned Aerial Vehicle platform show that it gives system parameter estimates that are accurate enough and converge quickly enough that flight controllers can be designed in real-time based on the online identified model.

关键词： System identification experience replay unmanned aerial vehicle online-identification method time-varying system

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