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第37届中国控制会议

作者： Yang YU Hai-Long PEI Cheng-Zhong XU Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationUnmanned Aerial Vehicle Systems Engineering Technology Research Center of GuangdongCollege of Automation Science and EngineeringSouth China University of Technology LAGEP Batiment CPEUniversity Claude Bernard-Lyon 1

The paper deals with the observation problem by using an infinite dimensional observer for the water wave system governed by linearized water wave partial differential *** consider that the water wave system evolves in some infinitedimensional Hilbert *** output measurement of dynamical system is supposed to be the water wave surface *** infinite-dimensional exponentially convergent Luenberger-like observer is proposed to estimate the states of water water system:water velocity potential and surface *** simulations by the finite difference scheme are carried out to validate the proposed observer.

关键词： infinite dimension system observer convergence finite difference method

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Hand Gesture Recognition using MYO Armband

Hand Gesture Recognition using MYO Armband

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Chinese Automation Congress (CAC)

作者： He, Shunzhan Yang, Chenguang Wang, Min Cheng, Long Hu, Zedong South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou Guangdong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781538635247

Surface electromyography (sEMG) is widely used in clinical diagnosis, rehabilitation engineering and human-computer interaction and other fields. In this paper, we use Myo armband to collect sEMG signals. Myo armband can be worn above any elbow of any arm and it can capture the bioelectric signal generated when the arm muscles move. MYO can pass of signals through its low-power Blue-tooth, and its interference is small, which makes the signal quality really good. By collecting the sEMG signals of the upper limb forearm, we extract five eigenvalues in the time domain, and use the BP neural network classification algorithm to realize the recognition of six gestures in this paper. Experimental results show that the use of MYO for gesture recognition can get a very good recognition results, it can accurately identify the six hand movements with the average recognition rate of 93%.

关键词： sEMG Myo Armband Gesture Recognition Feature Extraction BP Neural Network

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Adaptive control of a Marine Vessel Based on Reinforcement Learning 37

Adaptive Control of a Marine Vessel Based on Reinforcement L...

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37th Chinese control Conference (CCC)

作者： Yin, Zhao He, Wei Sun, Changyin Li, Guang Yang, Chenguang Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Queen Mary Univ London Mile End Rd London E1 4NS England South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9789881563958

In this paper, our main goal is to solve optimal control problem by using reinforcement learning (RL) algorithm for marine surface vessel system with known dynamic. And this algorithm is an optimal control algorithm based on policy iteration (PI), and it can obtain the suitable approximations of cost function and the optimized control policy. There are two neural networks (NNs), where critic NN aims to estimate the cost-to-go and actor NN is utilized to design suitable input controller and minimize the tracking error. A novel tuning method is given for critic NN and actor NN. The stability and convergence are proven by Lyapunov's direct method. Finally, the numerical simulations are conducted to demonstrate the feasibility and superiority of presented algorithm.

关键词： Reinforcement Learning Critic Neural Networks Actor Neural Networks Lyapunov Method Marine Vessel

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Adaptive control of a Marine Vessel Based on Reinforcement Learning

Adaptive Control of a Marine Vessel Based on Reinforcement L...

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第37届中国控制会议

作者： Zhao Yin Wei He Changyin Sun Guang Li Chenguang Yang School of Automation and Electrical Engineering University of Science and Technology Beijing School of Automation Southeast University Queen Mary University of London Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology

In this paper, our main goal is to solve optimal control problem by using reinforcement learning（RL） algorithm for marine surface vessel system with known dynamic. And this algorithm is an optimal control algorithm based on policy iteration（PI）, and it can obtain the suitable approximations of cost function and the optimized control policy. There are two neural networks（NNs）, where critic NN aims to estimate the cost-to-go and actor NN is utilized to design suitable input controller and minimize the tracking error. A novel tuning method is given for critic NN and actor NN. The stability and convergence are proven by Lyapunov’s direct method. Finally, the numerical simulations are conducted to demonstrate the feasibility and superiority of presented algorithm.

关键词： Reinforcement Learning Critic Neural Networks Actor Neural Networks Lyapunov Method Marine Vessel

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Composite Learning for Trajectory Tracking control of Robot Manipulators with Output Constraints

Composite Learning for Trajectory Tracking Control of Robot ...

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International Conference on Information Science and Technology (ICIST)

作者： Dianye Huang Chenguang Yang Yongping Pan Shilu Dai Zhaojie Ju Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Biomedical Engineering National University of Singapore Singapore Singapore School of Computing University of Portsmouth Portsmouth UK

In this paper, a trajectory tracking scheme for robot manipulators with unknown dynamics is investigated, with the consideration of output constraints as well as small bounded external disturbances. Firstly, a modified backstepping control scheme is employed to control the robot manipulators where in the first step of the design a tan-type barrier Lyapunov candidate is chosen in order to tackle the constraint problem. Secondly, the philosophy of dynamic surface control is incorporated to implement the calculation of prediction errors, which can also reduce “explosion of complexity” of the backstepping scheme. In addition, composite learning is introduced for a better estimation of unknown parameters, and for canceling out the uncertainties of the robot manipulators. Stability analysis shows that the proposed control scheme guarantees a small bounded tracking error with parameter convergence in the absence of the stringent persistent excitation condition. Finally, a simulation is conducted and the results demonstrate the superiority of the proposed controller in the aspects of tracking capability and parameter estimation.

关键词： Manipulator dynamics Backstepping Uncertainty control systems Convergence

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Adaptive Parameter Estimation and control Design for Robot Manipulators With Finite-Time Convergence

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第10期65卷 8112-8123页

作者： Yang, Chenguang Jiang, Yiming He, Wei Na, Jing Li, Zhijun Xu, Bin South China Univ Technol Key Lab Autonomous Syst & Networked Control Guangzhou 510641 Guangdong Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650093 Yunnan Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China Northwest Polytech Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China

For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few works of literature are concerned with the convergence speed. In this paper, we developed a robot control/identification scheme to identify the unknown robot kinematic and dynamic parameters with enhanced convergence rate. Superior to the traditional methods, the information of parameter estimation error was properly integrated into the proposed identification algorithm, such that enhanced estimation performance was achieved. Besides, the Newton-Euler (NE) method was used to build the robot dynamic model, where a singular value decomposition-based model reduction method was designed to remedy the potential singularity problems of the NE regressor. Moreover, an interval excitation condition was employed to relax the requirement of persistent excitation condition for the kinematic estimation. By using the Lyapunov synthesis, explicit analysis of the convergence rate of the tracking errors and the estimated parameters were performed. Simulation studies were conducted to show the accurate and fast convergence of the proposed finite-time (FT) identification algorithm based on a 7-DOF arm of Baxter robot.

关键词： Adaptive control finite time robotic manipulator parameter estimation unknown kinematics and dynamics

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ROBUST MPC FOR LINEAR systems WITH STRUCTURED TIME-VARYING UNCERTAINTIES AND SATURATING ACTUATOR

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ASIAN JOURNAL OF control 2017年第3期19卷 1197-1204页

作者： Zhang, Langwen Xie, Wei Wang, Jingcheng South China Univ Technol Coll Automat Sci & Technol Guangzhou 510640 Guangdong Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

In this paper, a synthesis of model predictive control (MPC) algorithm is presented for uncertain systems subject to structured time-varying uncertainties and actuator saturation. The system matrices are not exactly known, but are affine functions of a time varying parameter vector. To deal with the nonlinear actuator saturation, a saturated linear feedback control law is expressed into a convex hull of a group of auxiliary linear feedback laws. At each time instant, a state feedback law is designed to ensure the robust stability of the closed-loop system. The robust MPC controller design problem is formulated into solving a minimization problem of a worst-case performance index with respect to model uncertainties. The design of controller is then cast into solving a feasibility of linear matrix inequality (LMI) optimization problem. Then, the result is further extended to saturation dependent robust MPC approach by introducing additional variables. A saturation dependent quadratic function is used to reduce the conservatism of controller design. To show the effectiveness, the proposed robust MPC algorithms are applied to a continuous-time stirred tank reactor (CSTR) process.

关键词： Robust model predictive control structured uncertain systems polytopic uncertain actuator saturation saturation dependent

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Robust ADRC for Nonlinear Time-varying System with Uncertainties 6

Robust ADRC for Nonlinear Time-varying System with Uncertain...

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2017 IEEE 6th Data Driven control and Learning systems Conference (DDCLS’17)

作者： Xiangyang Li Wei Ai Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology IEEE Center for Advanced Control Technologies Department of Electrical and Computer EngineeringCleveland State University

ISBN: (纸本)9781509054626

Active disturbance rejection control(ADRC) exemplifies the spirit of the data-driven control(DDC) design strategy and shows much promise in obtaining consistent applications in industrial control systems with uncertainties, without the premise that the detailed mathematical model of the controlled system is given. Instead, it is shown that the information needed for the control system to work at high level of effectiveness can be extracted from the input-output data by the use of the extended state observer(ESO). On the other hand, it is shown in this paper that the robustness of ADRC depends on the effectiveness of ESO. Furthermore, taking advantage of the rich body of knowledge in the existing field of robust control, the estimation error in ESO is analysed and, for the purpose of improved robustness, a unique nonlinear component is added to the conventional ADRC law. The modified ADRC which is a kind of robust ADRC law is validated in simulation for a nonlinear time-varying system with parametric and functional uncertainties. It is shown that the proposed robust ADRC law provides more effective tracking performance than the conventional ADRC when the bandwidth of ESO is not wide enough.

关键词： Active Disturbance Rejection control(ADRC) Robust control Extended State Observer(ESO) Nonlinear Time-varying System

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Type-z0g3 controller of Output Tracking for Fractional Power System 3

Type-z0g3 Controller of Output Tracking for Fractional Power...

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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Fangzheng Lai Penghao He Jinjin Wang Dechao Chen School of Information Science and Technology Sun Yat-sen University(SYSU) SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Because most systems are nonlinear in practical applications,output tracking for nonlinear system is of great interest to engineers and *** a specific class of nonlinear system,fractional po

ISBN: (纸本)9781509053643;9781509053636

Because most systems are nonlinear in practical applications,output tracking for nonlinear system is of great interest to engineers and *** a specific class of nonlinear system,fractional po

关键词： Trajectory Nonlinear systems Simulation Power system stability Sun

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From Davidenko Method to Zhang Dynamics for Nonlinear Equation systems Solving

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2017年第11期47卷 2817-2830页

作者： Zhang, Yunong Zhang, Yinyan Chen, Dechao Xiao, Zhengli Yan, Xiaogang SYSU 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

The solving of nonlinear equation systems (e.g., complex transcendental dispersion equation systems in waveguide systems) is a fundamental topic in science and engineering. Davidenko method has been used by electromagnetism researchers to solve time-invariant nonlinear equation systems (e.g., the aforementioned transcendental dispersion equation systems). Meanwhile, Zhang dynamics (ZD), which is a special class of neural dynamics, has been substantiated as an effective and accurate method for solving nonlinear equation systems, particularly time-varying nonlinear equation systems. In this paper, Davidenko method is compared with ZD in terms of efficiency and accuracy in solving time-invariant and time-varying nonlinear equation systems. Results reveal that ZD is a more competent approach than Davidenko method. Moreover, discrete-time ZD models, corresponding block diagrams, and circuit schematics are presented to facilitate the convenient implementation of ZD by researchers and engineers for solving time-invariant and time-varying nonlinear equation systems online. The theoretical analysis and results on Davidenko method, ZD, and discrete-time ZD models are also discussed in relation to solving time-varying nonlinear equation systems.

关键词： Comparison Davidenko method time-invariant nonlinear equation systems time-varying nonlinear equation systems Zhang dynamics (ZD)

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