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34th Chinese control Conference, CCC 2015

作者： Bolin, Liao Ying, Fang Yinyan, Zhang Hongzhou, Tan Yunong, Zhang Guangzhou China SYSU-CMU Shunde International Joint Research Institute Foshan China College of Information Science and Engineering Jishou University Jishou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou China

ISBN: (纸本)9789881563897

Helicopter sling load has a wide range of applications. However, the sling load of helicopter may make it dangerous. In this paper, the spot hover control and the swing control of helicopter sling load system are investigated. To solve the problems, the ZG method, which is the combination of Zhang dynamics (ZD) and gradient dynamics (GD) methods, is adopted. Firstly, with the purpose of helicopter spot hover considered, a z2g1 controller is designed and proposed by using the ZG method, where ZD is used for two times and GD is used for one time. Secondly, with the purpose of swing control of helicopter sling load considered, another z2g1 controller is designed and proposed. Simulation results on a mathematical model of helicopter sling load system equipped with the proposed z2g1 controllers are presented for different control purposes, which substantiate the effectiveness and significance of the proposed method and controllers. © 2015 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Helicopter Sling Load Spot Hover Swing control ZG (Zhang-Gradient) Method

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Approximate Stiffness Element Method for Complex Section Beam Modeling

Approximate Stiffness Element Method for Complex Section Bea...

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2020 International Conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2020

作者： Huang, Xinshen Li, Hao Zhang, Zhenkun Zheng, Jianguo Pei, Hailong Guangzhou Customs Technology Center Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou China

In this paper, a finite element method of simulating complex section beam element with approximate stiffness element is presented. By establishing an approximate stiffness element, the basic mechanical properties of the element are equivalent to the original complex section beam element's. The approximate stiffness element has several sub-section beam elements, which intersect with other beam elements in space. The problem that missing of intersection points among complex section beam and other beams, which is caused by using conventional beam element, is solved. The error between the approximate stiffness element and the original complex section beam element is analyzed by theoretical deduction, and the feasibility and accuracy of the approximate stiffness element method are verified by the physical test of a bus body. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Stiffness

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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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Infinitely many Zhang functions resulting in various ZNN models for time-varying matrix inversion with link to Drazin inverse

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INFORMATION PROCESSING LETTERS 2015年第9期115卷 703-706页

作者： Zhang, Yunong Qiu, Binbin Jin, Long Guo, Dongsheng Yang, Zhi 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

In this Letter, by generalizing the notion of Zhang functions (ZFs) from previous work, a novel general-form Zhang function (NGFZF) is proposed, developed and investigated. Specifically, based on the NGFZF, infinitely many ZFs (as error functions) can be readily generated by successively selecting the different values of its parameters. Besides, by employing the NGFZF, a novel general-form Zhang neural net (NGFZNN) is proposed and studied for real-time solution of a time-varying matrix inverse (also termed, Zhang matrix inverse, ZMI). Moreover, a link between ZMI and Drazin inverse is discovered and further generalized to solve for the time-varying Drazin inverse (TVDI). (C) 2015 Elsevier B.V. All rights reserved.

关键词： Parallel algorithms Real-time systems Zhang neural net (ZNN) Error functions Zhang matrix inverse (ZMI) Time-varying Drazin inverse (TVDI)

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Robot Learning from Multiple Demonstrations with Dynamic Movement Primitive 2

Robot Learning from Multiple Demonstrations with Dynamic Mov...

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2nd International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Chen, Chuize Yang, Chenguang Zeng, Chao Wang, Ning Li, Zhijun South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781538632604

In this paper, we present a method for robot to learn point-to-point motions from human demonstrations. The motion is modelled as a nonlinear dynamic system called dynamic movement primitive (DMP). The original DMP can be only used to learn from single demonstration. In order to learn from multiple demonstrations of a specific task, we combine the DMP with Gaussian mixture models (GMMs), and the nonlinear part of the DMP is learned through Gaussian mixture regression (GMR). Thus more features of the same skill can be extracted to generate a better motion, and good performance of the original DMP, e.g., the ability of generalization, spatial and temporal scaling, is inherited. A motion capture sensor is used in this work to extract human tutor's demonstrations. The effectiveness of the developed method is verified based on a virtual Baxter robot platform.

关键词： Robot learning Dynamic movement primitive Gaussian mixture models and Gaussian mixture regression

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Matlab Simulink of Varying-Parameter Convergent-Differential Neural-Network for Solving Online Time-Varying Matrix Inverse 9

Matlab Simulink of Varying-Parameter Convergent-Differential...

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9th International Symposium on Computational Intelligence and Design (ISCID)

作者： Zhang, Zhijun Chen, Siyuan Zheng, Lunan Zhang, Jiayu South China Univ Technol Sch Automat Sci & Engn Guangzhou Guangdong Peoples R China South China Univ Technol Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781509035588

To deal with time-varying equations, a novel recurrent neural network, named varying-parameter convergent-differential neural network (in short, VP-CDNN), is proposed, modeled and analyzed. It is designed by a matrix-valued error function and the design parameter is time-varying, which enables the VP-CDNN to have good convergence and robustness. For illustration and comparison, a scalar-valued error function-based recurrent neural network, i.e., gradient-based neural network (GNN), is presented and developed. Matlab Simulink of both the VP-CDNN and GNN for solving online time-varying matrix inverse is constructed and presented. Comparison results verify that the proposed VP-CDNN is more effective and robust than those of GNN.

关键词： neural network matrix inverse time-varying equation neural dynamics convergence robustness

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OD-SLAM: Real-Time Localization and Mapping in Dynamic Environment through Multi-Sensor Fusion 5

OD-SLAM: Real-Time Localization and Mapping in Dynamic Envir...

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5th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Xu, Hua Yang, Chenguang Li, Zhijun South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781728164793

Only when the robot knows its position can it execute some specific functions. But in many cases, the robot faces an unknown environment and doesn't know where it is. Visual SLAM system uses the camera on the robot to capture the image of the surrounding environment for positioning. This is a method that relies on external information for positioning. When the external information is dynamic, the pose estimated by the traditional visual SLAM system is inaccurate. So a novel SLAM system is proposed in this paper, which mainly uses a neural network and multi-sensor fusion to realize robot positioning in a dynamic environment. The SLAM system is developed based on ORB-SLAM2. It recognizes highly dynamic objects in the image through the neural network and removes them from the image. Because the neural network can't accurately identify dynamic objects every time, it will affect the pose estimation of the SLAM system. Therefore, the SLAM system proposed in this paper uses odometer to constrain features extracted in the image, so as to obtain static features, and finally estimate a more accurate pose. Finally, the experimental results show that our system has better real-time and accuracy compared with the traditional visual SLAM systems and other dynamic SLAM systems.

关键词： Real time systems

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Observer-based Dynamic Surface control for Robotic Manipulator with Fixed-time Convergence 7

Observer-based Dynamic Surface Control for Robotic Manipulat...

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7th International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2020

作者： Zhu, Chengzhi Zuo, Lei Wang, Ning Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou510640 China School of Electronic and Control Engineering Chang'an University Xi'an710064 China School of Information Engineering Chang'an University Xi'an710064 China

ISBN: (纸本)9781728162461

A state feedback dynamic surface tracking control algorithm is proposed for parametric linearized manipulator model in this paper. Different from ordinary finite-time control schemes, fixed-time convergence can be achieved in the proposed algorithm via both of high and low-degree terms in the controller such that the settling time can be preestablished by the designed parameters independent of the system initial conditions. Moreover, the problem of the 'explosion of complexity' and the singularity can be avoided by constructing an auxiliary controller with a first-order filter. The semi-global fixed-time uniform convergence of all the signals in the closed-loop system can be proved by Lyapunov theorem. In order to avoid the employment of acceleration signals and obtain better tracking performance during the procedure, a kind of observer based on uniform exact differentiators was used to compensate the uncertainties online. Finally the superiority of the proposed method has been verified by simulations based on a 2-DOF robot manipulator. © 2020 IEEE.

关键词： State feedback

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Neural-learning Enhanced Admittance control of a Robot Manipulator With Input Saturation

Neural-learning Enhanced Admittance Control of a Robot Manip...

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Chinese Automation Congress (CAC)

作者： Peng, Guangzhu Yang, Chenguang He, Wei Li, Zhijun Kuang, Donghai South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou Guangdong Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781538635247

In this paper, a sensorless admittance control scheme is developed for robot manipulators in the presence of input saturation by employing neural networks. To deal with system uncertainties, the radial basis function neural network (RBFNN) is integrated into control design. In order to deal with input saturation, a compensator is applied to handle this problem. To interact with the environment, admittance control is employed and external torque is estimated by using a generalized momentum based disturbance observer. Simulations are performed to verify the effectiveness of the proposed control scheme.

关键词： Torque Manipulators Admittance control systems Robot sensing systems Artificial neural networks

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Stepsize Range and Optimal Value for Taylor-Zhang Discretization Formula Applied to Zeroing Neurodynamics Illustrated via Future Equality-Constrained Quadratic Programming

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2019年第3期30卷 959-966页

作者： Zhang, Yunong Gong, Huihui Yang, Min Li, Jian Yang, Xuyun 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 Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China

In this brief, future equality-constrained quadratic programming (FECQP) is studied. Via a zeroing neurodynamics method, a continuous-time zeroing neurodynamics (CTZN) model is presented. By using Taylor-Zhang discretization formula to discretize the CTZN model, a Taylor-Zhang discrete-time zeroing neurodynamics (TZ-DTZN) model is presented to perform FECQP. Furthermore, we focus on the critical parameter of the TZ-DTZN model, i.e., stepsize. By theoretical analyses, we obtain an effective range of the stepsize, which guarantees the stability of the TZ-DTZN model. In addition, we further discuss the optimal value of the stepsize, which makes the TZ-DTZN model possess the optimal stability (i.e., the best stability with the fastest convergence). Finally, numerical experiments and application experiments for motion generation of a robot manipulator are conducted to verify the high precision of the TZ-DTZN model and the effective range and optimal value of the stepsize for FECQP.

关键词： Future equality-constrained quadratic programming (FECQP) motion generation of robot manipulator optimal value stepsize range Taylor-Zhang discretization formula zeroing neurodynamics

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