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Compliant Impedance control for a Redundant Manipulator During Human Robot Interaction 24

Compliant Impedance Control for a Redundant Manipulator Duri...

24th IEEE International Conference on Automation and Computing (ICAC) - Improving Productivity through Automation and Computing Newcastle

作者： Jiang, Yiming Yang, Chenguang Ju, Zhaojie Annamalai, Andy Liu, Honghai South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Univ Portsmouth Sch Comp Portsmouth PO1 3HE Hants England Swansea Univ Zienkiewicz Ctr Computat Engn Swansea SA1 8EN W Glam Wales Univ Highlands & Isl Moray Coll Elgin IV30 1JJ Scotland

ISBN: (纸本)9781862203426

Robot control with a compliant motion behaviour is important to guarantee the safety of human robot interaction. In this paper, we aim to guarantee the desired task impedance of the end-effector and to ensure a compliant behaviour of a redundant robot during human robot interactions. A task space impedance control scheme is proposed by designing a transferred impedance error, such that the robot end-effector can be governed to follow a target impedance model. Additionally, a low-priority controller is designed using the null space projection, such that compliant joint motion is guaranteed without affecting the main task. The stability of the whole system is illustrated using a conditional Lyapunov theorem. Simulation studies based on a plane redundant robot is carried out to demonstrate the validity of the proposed controller.

关键词： Redundant Robot Human-robot Interaction Null Space control Impedance control

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Compliant Impedance control for a Redundant Manipulator During Human Robot Interaction

Compliant Impedance Control for a Redundant Manipulator Duri...

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International Conference on Automation and Computing (ICAC)

作者： Yiming Jiang Chenguang Yang Zhaojie Ju Andy Annamalai Honghai Liu Key Laboratory of Autonomous Systems and Networked Control South China University of Technology Guangzhou China Zienkiewicz Center for Computational Engineering Swansea University Swansea UK School of Computing University of Portsmouth Portsmouth UK Moray College University of Highlands and Islands Elgin UK

ISBN: (纸本)9781538648919

关键词： Robots Impedance Task analysis Aerospace electronics Null space Torque Force

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Robust distributed model predictive control of linear systems with structured time-varying uncertainties

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INTERNATIONAL JOURNAL OF control 2017年第11期90卷 2449-2460页

作者： Zhang, Langwen Xie, Wei Wang, Jingcheng South China Univ Technol Coll Automat Sci & Technol Guangzhou Guangdong Peoples R China South China Univ Technol Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou Guangdong Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.

关键词： Distributed MPC robust control structured uncertain systems iterative algorithm

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A Hybrid Multi-Objective Scheme Applied to Redundant Robot Manipulators

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2017年第3期14卷 1337-1350页

作者： Chen, Dechao Zhang, Yunong 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 paper, a hybrid multi-objective scheme is proposed to complete simultaneously four objectives, i.e., the specified primary task for the end-effector, obstacle avoidance, joint-physical limits avoidance, and repetitive motion of redundant robot manipulators. In addition, corresponding theoretical analysis is given, which guarantees the validity of the proposed scheme. Then, the proposed hybrid multi-objective scheme is reformulated as a dynamical quadratic program (DQP) problem. The optimal solution of the DQP problem is found by the PLPE (piecewise-linear projection equation) neural network, i.e., PLPENN, and also by the corresponding numerical algorithm implemented on the computer. Furthermore, simulation and comparison based on a six-link planar redundant robot manipulator substantiate the effectiveness and accuracy of the proposed scheme. At last, a hardware experiment is conducted on a six-link physical robot manipulator system, which substantiates the physical realizability, operational stability, and safety of the proposed hybrid multi-objective scheme. Note to Practitioners-This paper is motivated by the multi-objective problem of redundant robot manipulators in practical applications. Note that redundant robot manipulators are usually required to handle the multiple objectives simultaneously in a complex environment. Thus, a physically realizable, operationally stable, and safe solution for such redundant robot manipulators is significant for practitioners. This paper proposes a hybrid multi-objective scheme to complete simultaneously multiple objectives of redundant robot manipulators. Besides, for the convenience of practitioners to develop in applications, the corresponding numerical algorithm with its block diagram given for the optimal solution of the proposed scheme is also presented. Computer simulation based on a six-link planar redundant robot manipulator shows that the four mentioned objectives are completed well. The proposed scheme is als

关键词： Hybrid multi-objective scheme joint-physical limits avoidance obstacle avoidance redundant robot manipulators repetitive motion

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Simpler ZD-achieving controller for chaotic systems synchronization with parameter perturbation, model uncertainty and external disturbance as compared with other controllers

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OPTIK 2017年 131卷 364-373页

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

The synchronization of chaotic systems has been studied recently due to its fundamental role in engineering fields. However, many conventional synchronization controllers are designed without considering the parameter perturbation, model uncertainty or external disturbance and the design processes of most conventional controllers are relatively quite complicated. Motivated by the above considerations and inspired by the zeroing dynamics (ZD) method, a quite simple and effective ZD-achieving controller is designed for the synchronization of chaotic systems with the simultaneous existence of parameter perturbation, model uncertainty and external disturbance. Theoretical analysis and numerical simulations show the strong robustness of the ZD-achieving controller, and show that the precision can be enhanced by adjusting the value of the parameter in the controller. Moreover, a modified ZD-achieving controller is designed to further improve the ZD-achieving controller. Besides, for comparison, the conventional ZD-derived controller and the linear active controller are presented. Finally, both the synchronization of two isomorphic chaos systems and the synchronization of two heteroideous chaos systems are investigated to substantiate the effectiveness and superiority of the ZD-achieving controller. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： ZD-achieving controller Synchronization Chaotic systems Parameter perturbation Model uncertainty

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Euler-Discretized GZ-Type Complex Neuronet Computing Real-Time Varying Complex Matrix Inverse 36

Euler-Discretized GZ-Type Complex Neuronet Computing Real-Ti...

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第36届中国控制会议

作者： Yunong Zhang Guofu Wu Binbin Qiu Wan Li Penghao He School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Reseach Institute Key Laboratory of Machine Intelligence and Advanced Computing(Sun Yat-sen University) Ministry of Education

ISBN: (纸本)9781538629185

In this paper,a GZ-type complex neuronet（generated by combining gradient dynamic system and Zhang dynamic system） is illustrated and discretized into an Euler-discretized（i.e.,discretized by Euler forward rule） GZ-type complex neuronet for computing real-time varying complex matrix *** results not only show the efficacy of the proposed Eulerdiscretized GZ-type neuronet but also verify that the error pattern is square.

关键词： GZ-Type Neuronet Complex Matrix Inverse Euler Forward Rule Computer Simulation Error Pattern

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Signum-function array activated ZNN with easier circuit implementation and finite-time convergence for linear systems solving

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INFORMATION PROCESSING LETTERS 2017年第Aug.期124卷 30-34页

作者： Zhang, Yunong Ding, Yaqiong Qiu, Binbin Zhang, Yinyan Li, Xiaodong Sun Yat Sen Univ Sch Data & Comp Sci 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

A new type of Zhang neural network (ZNN), which is activated by the signum-function array, is proposed for linear systems solving. Such a signum-function array activated ZNN is developed on the basis of a vector-valued error function instead of a scalar-valued norm based energy function. Besides, a theorem is provided to illustrate the excellent finite time convergence property of the new-type ZNN. In addition, the corresponding circuit schematic of the signum-function array activated ZNN is given. For better illustration, a representative simulative example is presented and the corresponding simulation result is shown to substantiate the efficacy of the proposed new-type ZNN for linear systems solving. Besides, the comparative simulation result further shows the desired finite-time convergent performance. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Algorithms Real-time systems Zhang neural network (ZNN) Signum-function array Linear systems solving Finite-time convergence

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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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Adaptive fuzzy control for a marine vessel with time-varying constraints

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IET control THEORY AND APPLICATIONS 2018年第10期12卷 1448-1455页

作者： Kong, Linghuan He, Wei Yang, Chenguang Li, Guang Zhang, Zhengqiang 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 Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Queen Mary Univ London Sch Engn & Mat Sci Mile End Rd London E1 4NS England Qufu Normal Univ Sch Elect Engn & Automat Rizhao 276826 Peoples R China

An adaptive fuzzy neural network (FNN) control scheme is proposed for a marine vessel with time-varying constraints, guaranteed transient response and unknown dynamics. A series of continuous constraint functions are introduced to shape the motion of a marine vessel. To deal with the constraint problems and transient response problems, an asymmetric time-varying barrier Lyapunov function is designed to ensure that the system states are upper bounded by the considered constraint functions. FNNs are constructed to identify the unknown dynamics. Considering existing approximation errors when FNNs approximating the unknown dynamics, an adaptive term is designed to compensate the approximation errors in order to obtain accurate control. Via Lyapunov stability theory, it has been proved that all the states in the closed-loop system are uniformly bounded ultimately without violating the corresponding prescribed constraint region. Two comparative simulations are carried out to verify the effectiveness of the proposed control.

关键词： neurocontrollers transient response stability adaptive control Lyapunov methods time-varying systems closed loop systems fuzzy neural nets fuzzy control marine vehicles control system synthesis approximation theory time-varying constraints adaptive fuzzy neural network control scheme marine vessel guaranteed transient response unknown dynamics continuous constraint functions constraint problems transient response problems asymmetric time-varying barrier Lyapunov function approximation errors adaptive term adaptive fuzzy control constraint functions FNN system states Lyapunov stability theory closed-loop system prescribed constraint region

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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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