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General Square-Pattern Discretization Formulas via Second-Order Derivative Elimination for Zeroing Neural Network Illustrated by Future Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2019年第3期30卷 891-901页

作者： Li, Jian Zhang, Yunong Mao, Mingzhi 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

Previous works provide a few effective discretization formulas for zeroing neural network (ZNN), of which the precision is a square pattern. However, those formulas are separately developed via many relatively blind attempts. In this paper, general square-pattern discretization (SPD) formulas are proposed for ZNN via the idea of the second-order derivative elimination. All existing SPD formulas in previous works are included in the framework of the general SPD formulas. The connections and differences of various general formulas are also discussed. Furthermore, the general SPD formulas are used to solve future optimization under linear equality constraints, and the corresponding general discrete ZNN models are proposed. General discrete ZNN models have at least one parameter to adjust, thereby determining their zero stability. Thus, the parameter domains are obtained by restricting zero stability. Finally, numerous comparative numerical experiments, including the motion control of a PUMA560 robot manipulator, are provided to substantiate theoretical results and their superiority to conventional Euler formula.

关键词： Future optimization general square-pattern discretization (SPD) formulas second-order derivative elimination (SODE) zero stability zeroing neural network (ZNN)

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Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands

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autonomous ROBOTS 2020年第7期44卷 1217-1231页

作者： Huang, Darong Yang, Chenguang Ju, Zhaojie Dai, Shi-Lu South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China Univ West England Bristol Robot Lab Bristol BS16 1QY Avon England Univ Portsmouth Sch Comp Portsmouth POI 2DJ Hants England

Disturbance observer (DOB) based controller performs well in estimating and compensating for perturbation when the external or internal unknown disturbance is slowly time varying. However, to some extent, robot manipulators usually work in complex environment with high-frequency disturbance. Thereby, to enhance tracking performance in a teleoperation system, only traditional DOB technique is insufficient. In this paper, for the purpose of constructing a feasible teleoperation scheme, we develop a novel controller that contains a variable gain scheme to deal with fast-time varying perturbation, whose gain is adjusted linearly according to human surface electromyographic signals collected from Myo wearable armband. In addition, for tracking the motion of operator's arm, we derive five-joint-angle data of a moving human arm through two groups of quaternions generated from the armbands. Besides, the radial basis function neural networks and the disturbance observer-based control (DOBC) approaches are fused together into the proposed controller to compensate the unknown dynamics uncertainties of the slave robot as well as environmental perturbation. Experiments and simulations are conducted to demonstrated the effectiveness of the proposed strategy.

关键词： Disturbance observer Motion capture Radial basis function neural networks Teleoperation Variable gain control

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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

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

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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Common nature of learning between BP-type and Hopfield-type neural networks

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NEUROCOMPUTING 2015年 167卷 578-586页

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

Being two famous neural networks, the error back-propagation (BP) algorithm based neural networks (i.e., BP-type neural networks, BPNNs) and Hopfield-type neural networks (HNNs) have been proposed, developed, and investigated extensively for scientific research and engineering applications. They are different from each other in a great deal, in terms of network architecture, physical meaning and training pattern. In this paper of literature-review type, we present in a relatively complete and creative manner the common natures of learning between BP-type and Hopfield-type neural networks for solving various (mathematical) problems. Specifically, comparing the BPNN with the HNN for the same problem-solving task, e.g., matrix inversion as well as function approximation, we show that the BPNN weight-updating formula and the HNN state-transition equation turn out to be essentially the same. Such interesting phenomena promise that, given a neural-network model for a specific problem solving, its potential dual neural-network model can thus be developed. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Neural networks Common nature of learning BP-type Hopfield-type Problem solving

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Distributed data-driven consensus control of multi-agent systems under switched uncertainties

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control Theory and Technology 2023年第3期21卷 478-487页

作者： Wenjie Liu Yifei Li Gang Wang Jian Sun Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

Cooperative control of multi-agent systems(MASs),particularly consensus control,has gained significant attention in the last two decades,thanks to the rapid and sustained development of distributed and networked *** this paper,we present some new results focused on consensus control of a set of unknown linear MASs(whose system matrices are unknown)under unknown switched uncertainties,with an emphasis on distributed data-driven *** proposed controller is end-to-end,designed by solving two data-based semi-definite programs(SDPs),which adjust to the changes of the uncertainty *** approach achieves asymptotic consensus of the MAS provided that the switching is slow enough and the uncertainty is *** illustrate the effectiveness of our proposed method through a numerical example.

关键词： Data-driven control Multi-agent systems Consensus-Switched uncertainties

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A Learning Framework of Adaptive Manipulative Skills From Human to Robot

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2019年第2期15卷 1153-1161页

作者： Yang, Chenguang Zeng, Chao Cong, Yang Wang, Ning Wang, Min 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 110016 Liaoning Peoples R China Plymouth Univ Ctr Robot & Neural Syst Plymouth PL4 8AA Devon England

Robots are often required to generalize the skills learned from human demonstrations to fulfil new task requirements. However, skill generalization will be difficult to realize when facing with the following situations: the skill for a complex multistep task includes a number of features;some special constraints are imposed on the robots during the process of task reproduction;and a completely new situation quite different with the one in which demonstrations are given to the robot. This work proposes a new framework to facilitate robot skill generalization. The basic idea lies in that the learned skills are first segmented into a sequence of subskills automatically, then each individual subskill is encoded and regulated accordingly. Specifically, we adapt each set of the segmented movement trajectories individually instead of the whole movement profiles, thus, making it more convenient for the realization of skill generalization. In addition, human limb stiffness estimated from surface electromyographic signals is considered in the framework for the realization of human-to-robot variable impedance control skill transfer, as well as the generalization of both movement trajectories and stiffness profiles. Experimental study has been performed to verify the effectiveness of the proposed framework.

关键词： Adaptive stiffness control human-robot skill transfer skill segmentation stiffness generalization

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Mean policy-based proximal policy optimization for maneuvering decision in multi-UAV air combat

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Neural Computing and Applications 2024年第31期36卷 19667-19690页

作者： Zheng, Yifan Xin, Bin He, Bin Ding, Yulong Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China School of Automation Beijing Institute of Technology Beijing China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

autonomous maneuvering decision-making is a crucial technology for Unmanned Aerial Vehicles (UAVs) to take the air domination in modern unmanned warfare. With the advantage of balancing exploration and exploitation, as well as the immediacy of end-to-end output by combining with deep neural network, multi-agent reinforcement learning (MARL) has made remarkable achievements in multi-UAV autonomous air combat maneuvering decision-making (MUAAMD). However, the implementation of effective cooperative policy learning remains a challenging issue for MARL methods with centralized training decentralized execution (CTDE) paradigm. This paper proposes a MARL-based method to improve the performance of cooperation in MUAAMD. Firstly, considering the constraints of dynamic and limited perception for UAVs in the realistic air combat scenario, the MUAAMD problem is formulated based on partially observable Markov game (POMG) model. Secondly, a novel efficient MARL algorithm named the mean policy-based proximal policy optimization (MP3O) is introduced. Specifically, a joint policy optimization mechanism is constructed by estimating the policies of neighboring agents in group as a mean-field approximation while training, which enables both centralized evaluation and improvement of cooperative policy under the CTDE paradigm. Thirdly, by combining with three improvement techniques, a cooperative decision-making framework for MUAAMD based on MP3O is proposed. Empirically, results of simulations and comparative experiments validate the effectiveness of proposed method in promoting cooperative policy learning in resolving MUAAMD problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Decision making

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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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Adaptive dynamic programming enhanced admittance control for robots with environment interaction and actuator saturation

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2021年第1期5卷 89-100页

作者： Zhan, Hong Huang, Dianye Yang, Chenguang South China Univ Technol Key Lab Autonomous Syst & Networked Control Minist Educ Guangzhou 510640 Peoples R China Tech Univ Munich Dept Informat D-85748 Munich Germany Univ West England Bristol Robot Lab Bristol BS16 1QY Avon England

This paper focuses on the optimal tracking control problem for robot systems with environment interaction and actuator saturation. A control scheme combined with admittance adaptation and adaptive dynamic programming (ADP) is developed. The unknown environment is modelled as a linear system and admittance controller is derived to achieve compliant behaviour of the robot. In the ADP framework, the cost function is defined with non-quadratic form and the critic network is designed with radial basis function neural network which introduces to obtain an approximate optimal control of the Hamilton-Jacobi-Bellman equation, which guarantees the optimal trajectory tracking. The system stability is analysed by Lyapunov theorem and simulations demonstrate the effectiveness of the proposed strategy.

关键词： Adaptive dynamic programming Admittance control Robot-environment interaction Actuator saturation Optimal control Neural network

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China Population Prediction with the Aid of Fast,Optimal and Accurate WASD Neuronet Compared with BP Neuronet 35

China Population Prediction with the Aid of Fast,Optimal and...

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第35届中国控制会议

作者： ZHANG Yunong HE Penghao WEN Jianfeng HE Liangyu TAN Hongzhou 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 Department of Computer Science Guangdong Polytechnic Normal University

ISBN: (纸本)9781509009107

Population is one of the most significant factors affecting the social and economic development of a ***,China is facing a big challenge of population *** is important for Chinese government to pay continuous attention to the population situation and take measures so as to keep the economic ***,the effectiveness of these measures depends crucially on the accurate prediction of China *** methods,including the feedforward neuronet equipped with back-propagation（BP） algorithm,have been proposed in order to improve the prediction ***,these methods possibly have their inherent *** this paper,the authors construct an enhanced feedforward neuronet,i.e.,a 3-layer feedforward power-activated neuronet（PAN） equipped with the weights-and-structure-determination algorithm（for short,WASD-PAN）,to predict China *** experiments show that China population will continue to increase at a low rate in the next *** authors also compare the WASD-PAN with the back-propagation neuronet BP-PAN,with numerical experimental results showing the excellent performance of the former.

关键词： China Population Prediction Weights-And-Structure-Determination（WASD） Back-Propagation（BP） Neuronet

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