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control Under Stochastic Multiplicative Uncertainties: Part II, Optimal Design for Performance

IEEE TRANSACTIONS ON AUTOMATIC control 2017年第3期62卷 1285-1300页

作者： Su, Weizhou Chen, Jie Fu, Minyue Qi, Tian South China Univ Technol Minist Educ Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510641 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Univ Newcastle Sch Elect & Comp Engn Newcastle NSW 2308 Australia Zhejiang Univ Zhengzhou 310027 Zhejiang Peoples R China

This paper studies the optimal control design problem for linear discrete-time systems with stochastic multiplicative uncertainties. These uncertainties are assumed to be present in the control inputs and modeled as independent and identically distributed (i.i.d.)random processes. The optimal performance under study is defined in the mean-square sense, referred to as the mean-square optimal H-2 performance. It is shown that the mean-square optimal H-2 control problem via state feedback can be solved using a mean-square stabilizing solution to a modified algebraic Riccati equation ( MARE). A necessary and sufficient condition for the existence of this solution is presented, which constitutes a generalization of the solution to the classic optimal H-2 state feedback design problem, whereas the latter can be obtained by solving an algebraic Riccati equation ( ARE). It is also proven that the optimal control design problem can be cast as an eigenvalue problem ( EVP). For the output feedback case with possible input delays, we show that the mean-square optimal H-2 control problem also amounts to solving an MARE, when the plant has no nonminimum phase zeros from the inputs to the measurement outputs. That is, the global optimal solution is obtained by solving an MARE incorporating the delays. The implication is that in this case a separation principle still holds.

关键词： AlgebraicRiccati equation optimal control output feedback stochastic multiplicative uncertainties

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Kinematic control of Redundant Manipulators Using Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2017年第10期28卷 2243-2254页

作者： Li, Shuai Zhang, Yunong Jin, Long Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China SYSU Carnegie Mellon Univ Shunde Int Joint Res In Shunde 528300 Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China

Redundancy resolution is a critical problem in the control of robotic manipulators. Recurrent neural networks (RNNs), as inherently parallel processing models for time-sequence processing, are potentially applicable for the motion control of manipulators. However, the development of neural models for high-accuracy and real-time control is a challenging problem. This paper identifies two limitations of the existing RNN solutions for manipulator control, i.e., position error accumulation and the convex restriction on the projection set, and overcomes them by proposing two modified neural network models. Our method allows nonconvex sets for projection operations, and control error does not accumulate over time in the presence of noise. Unlike most works in which RNNs are used to process time sequences, the proposed approach is model-based and training-free, which makes it possible to achieve fast tracking of reference signals with superior robustness and accuracy. Theoretical analysis reveals the global stability of a system under the control of the proposed neural networks. Simulation results confirm the effectiveness of the proposed control method in both the position regulation and tracking control of redundant PUMA 560 manipulators.

关键词： Kinematic control neural network nonconvex set recurrent neural networks (RNNs) redundant manipulator robot arm

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Type-Z0G1 controller Using Gradient Descent of State Vector for Output Tracking of Time-Invariant Linear System

Type-Z0G1 Controller Using Gradient Descent of State Vector ...

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Chinese Automation Congress (CAC)

作者： Zhang, Yunong Xiao, Mengling Qiu, Binbin Shi, Yang Xue, Zhongxian 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

ISBN: (纸本)9781538635247

Compared with Zhang dynamics (ZD) method, gradient dynamics (GI)) method, which is intrinsically feasible and efficient to solve time-invariant problems, could have a simpler hardware implementation. In this paper, different from conventional type-Z0G1 controller in Zhang-gradient (ZG) framework, which uses gradient descent of input vector, a type-Z0G1 controller using gradient descent of state vector is proposed as an attempt for output tracking of time-invariant linear (TIL) system. Firstly, we develop and investigate Z0G1 controllers based on different gradient descent methods. Then, numerical experiments illustrate that the type-Z0G1 controller using gradient descent of state vector can achieve output tracking control with high accuracy and rapid tracking rate.

关键词： Gradient dynamics Gradient descent Time-invariant linear (TIL) system Output tracking

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Global Adaptive Tracking control of Robot Manipulators Using Neural Networks with Finite-time Learning Convergence

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INTERNATIONAL JOURNAL OF control AUTOMATION AND systems 2017年第4期15卷 1916-1924页

作者： Yang, Chenguang Teng, Tao Xu, Bin Li, Zhijun Na, Jing Su, Chun-Yi South China Univ Technol Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou Guangdong Peoples R China Swansea Univ Zienkiewicz Ctr Computat Engn Swansea SA1 8EN W Glam Wales Northwestern Polytech Univ Sch Automat Xian Shaanxi Peoples R China Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming Yunnan Peoples R China

In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability. by integrating a switching mechanism. Moreover, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.

关键词： Finite-time learning convergence globally uniformly ultimate boundedness neural networks robot manipulators

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Transient Tracking Performance Guaranteed Neural control of Robotic Manipulators with Finite-Time Learning Convergence 24th

Transient Tracking Performance Guaranteed Neural Control of ...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Teng, Tao Yang, Chenguang He, Wei Na, Jing Li, Zhijun South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Kunming Univ Sci & Technol Kunming 650093 Yunnan Peoples R China

ISBN: (纸本)9783319701363;9783319701356

An adaptive finite-time (FT) neural control scheme is proposed for robotic manipulators, which could guarantee transient tracking performances in the presence of model uncertainties. With the introduction of an error transformation mechanism, the original constrained manipulator system can be transformed into an unrestricted system. Moreover, the FT neural learning algorithm motivated by the estimated weights error, under persistent excitation (PE) condition, can guarantee the estimated neural weights converge to a small neighborhoods around the optimal values in finite time. Subsequently, the adaptive FT neural controller could ensure uniformly ultimate boundedness of all the signals in the closed-loop system and guarantee prescribed tracking and neural learning performances. The simulation results are given to illustrate the feasibility of the algorithm and correctness of theoretical analysis.

关键词： Robotic manipulator Neural network Finite-time convergence Predefined performances

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Robot Teaching by Teleoperation Based on Visual Interaction and Neural Network Learning 9th

Robot Teaching by Teleoperation Based on Visual Interaction ...

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9th International Conference on Modelling, Identification and control (ICMIC)

作者： Xu, Yang Yang, Chenguang Zhong, Junpei Ma, Hongbin Zhao, Lijun Wang, Min South China Univ Technol Coll Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Guangdong Peoples R China Natl Inst Adv Ind Sci & Technol Tokyo Japan Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9781509065738

Traditional methods of Robot teaching require human demonstrators to program with a teaching pendant, which is a complex and time-consuming exercise. In this paper, we propose a novel method based on teleoperation which allows a demonstrator to train robot in an intuitive way. More specifically, at the beginning the demonstrator controls a robot by visual interaction. And then a learning algorithm based on radial basis function (RBF) network is used to transfer the demonstrator's motions to the robot. To verify the effectiveness of this developed methods, several simulation experiments have been carried out which based on Microsoft Kinect Sensor and the Virtual Robot Experimentation Platform (V-REP). The experimental results show that this method has achieved satisfactory performance. With the help of this method, the robot can not only complete the task autonomously after teaching, but also can learn the details of demonstrator's behavior.

关键词： Robot Teaching Teleoperation radial basis function (RBF) network

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Symbolic Solutions to Division by Zero Problem via Gradient Neurodynamics 24th

Symbolic Solutions to Division by Zero Problem via Gradient ...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Zhang, Yunong Gong, Huihui Li, Jian Huang, Huanchang Yin, Ziyu Sun Yat Sen Univ SYSU Sch Informat Sci & Technol Guangzhou 510006 Peoples R China Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 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 Peoples R China

ISBN: (纸本)9783319700908;9783319700892

Division by zero (DBZ) problem, or say, division singularity problem, has perplexed scientists and engineers in many fields for centuries. How to solve DBZ problem has actually been discussed for more than 1200 years. Despondingly, plenty of efforts failed to solve DBZ problem effectively, and it is still considered as a formidable conundrum. This paper introduce an extension of the division operation from a time-varying perspective. Most problems in science and engineering fields are time-varying, and thus the extension is reasonable and practical. Furthermore, by employing the neat gradient-based neurodynamics (or say, gradient neurodynamics) equation different times, a series of different "symbolic" solutions are proposed. Note that the proposed symbolic solutions have the ability to conquer DBZ problem. The symbolic solutions to DBZ problem may promote the development of more complete singularity-conquering applications.

关键词： Division by zero Gradient neurodynamics Singularity Symbolic solutions

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Manipulation of A Robot Arm in 3D Space by Using EEG Signals 2

Manipulation of A Robot Arm in 3D Space by Using EEG Signals

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

作者： Wu, Huaiwei Yang, Chenguang Wang, Ning He, Wei Su, Chun-Yi 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 Bldg Shenzhen Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781538632604

This paper proposes an innovative robot arm control method of 3D space manipulation via using Electroencephalograph (EEG) signals. This system contains three parts: the Brain-Computer Interface (BCI), visual feedback, and robot control platform. First, the BCI collects and analyzes EEG signals from a subject and then classifies them into two different robot control commands. Thereafter, a binocular camera is employed to capture the objects and robot end-effector positions, which are then abstracted into symbols, e.g., rectangles, lines, etc, and then displayed on a screen. From the screen, the subject can get the position feedback information of the robot hand as well as that of the object in order to make decisions accordingly. Finally, the robot control platform decomposes the 3D coordinate system into a lower dimensional coordinate system, therefore the robot hand can be controlled to move to any specific positions corresponding to the subject's EEG commands through forward and inverse kinematics theory.

关键词： BCI Manipulator Visual Servoing

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Different-Level Time-Varying Quadratic Minimization Using Zhang Equivalency and Moore-Penrose Pseudoinverse

Different-Level Time-Varying Quadratic Minimization Using Zh...

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Chinese Automation Congress (CAC)

作者： Zhang, Yunong Yang, Min Qiu, Binbin Huang, Huanchang Hu, Haifeng 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

ISBN: (纸本)9781538635247

Quadratic minimization (QM) has been investigated for many years, which is generally supposed to be time-invariant and at the same level. Nevertheless, the QM which arises in practical applications, is usually time-varying and may be at the different levels. Different from general QM, a novel different-level time-varying QM (DLTVQM) is proposed and investigated in this paper. Firstly, the DLTVQM solving is transformed into two general time-varying QM (TVQM) solving. Then, by applying Zhang equivalency and Moore-Penrose pseudoinverse, a solution model is proposed and investigated for the DLTVQM solving. It is worth pointing out that this paper is the first attempt to solve the novel DLTVQM. In addition, numerical experiments are conducted with results presented and analyzed.

关键词： Different-level time-varying quadratic minimization (DLTVQM) Zhang equivalency Moore-Penrose pseudoinverse Solution model

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Output Tracking of Time-Varying Linear System Using ZD controller with Pseudo Division-by-Zero Phenomenon Illustrated 43

Output Tracking of Time-Varying Linear System Using ZD Contr...

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43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Zhang, Yunong Guo, Jinjin Zhang, Deyang Qiu, Binbin Yang, Zhi 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

ISBN: (纸本)9781538611272

In this paper, the output tracking of time-varying linear system is investigated with pseudo division-by-zero phenomenon illustrated. We only use Zhang dynamics (ZD) to design a ZD tracking controller, which is easier than using Zhang-gradient (ZG) method. The purpose of output tracking control for the considered time-varying linear system is to make the actual output track the desired output with absolute error within an acceptably tiny error range;namely, the absolute error is as small as possible or even exponentially convergent to zero. By conducting controller design with ZD method as well as computer simulations, we can verify the efficacy of the proposed ZD controller in performing the output tracking control with the pseudo division-by-zero phenomenon illustrated. Besides, for the sake of comparative analysis of simulation results, we set the design parameters with different specific values. Anyhow, the output tracking of time-varying linear system can finally be achieved through simulation examples.

关键词： Output tracking Time-varying linear system Pseudo division-by-zero Zhang dynamics (ZD)

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