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第37届中国控制会议

作者： Liu Weidong Liu Haiming Guo Jinhui Key laboratory of autonomous systems and networked control Ministry of education South China university of technology

Considering the batch assembly with multiple function requirements（FR）, the multi-object selective assembly（SA） technology based on the hybrid particle swarm algorithm is introduced in this paper. Firstly, the SA model is discussed, and a multi-object optimization problem is illustrated based on the assembly chains;additionally, the hybrid particle swarm algorithm is used to solve the multi-object selective assembly problem. The matching matrix of batch parts is designed as the particle. The local non-inferior particles and the global non-inferior particles of each iteration population are calculated. The particle population is update by crossover and mutation operations. After all the iterations, the final global non-inferior particles are selected as the matching strategy of batch parts. Based on the optimized matching strategy, the assembly success rate is calculated, and each assembly accuracy and its precision is guaranteed. The process of multi-object SA based on hybrid particle swarm algorithm is introduced, and an example of batch cabin sections are illustrated to obtain the optimized matching strategy of batch parts. Contrasted to the sequential strategy and the random strategy, it can find that the multi-object SA method based on hybrid particle swarm algorithm has advantage in guaranteeing the assembly success rate, and comprehensively decreasing each assembly accuracy.

关键词： multi-object selective assembly hybrid particle swarm algorithm assembly chain non-inferior particles matching strategy of batch parts

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Human Motion Prediction Based on Visual Tracking 4

Human Motion Prediction Based on Visual Tracking

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4th International Conference on robotics and Automation Engineering, ICRAE 2019

作者： Zou, Juncheng Yin, Weihua Wang, Everett X. Wang, Jiancheng Lu, Yan-Feng School of Mechanical Engineering Beijing University of Science and Technology China Institute of Science and Technology Innovation Dongguan University of Technology China School of Information Engineering Guangdong University of Technology Guangzhou China Cloud Computing Center Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728147406

ISBN: (纸本)9781728147406

Following and moving according to human motion is an important task for mobile robots. To ensure more compliant motion planning and execution of mobile robots, they can not be controlled by real-time visual information. In the tracking process, robots need to recognize and track the target then, plan the motion and execute the motion. In practical applications, the environments are very complex, such as illumination, shadows and occlusion, which the traditional visual tracking algorithms often deviate or lose the target. Therefore, to achieve fast human motion tracking, it is necessary to predict human motions by video prediction. In this paper, we propose a human motion tracking algorithm of mobile robot based on video prediciton. (1) The multi-layer generation adversarial loop network, which trained by off-line video dataset and learn how to predict human motion. (2) We used the pre-trained model to predict the state of the tracking target in the video. (3) This video prediction model was integrated into human-to-robot tracking algorithms of mobile robot following human system. Experiments show that the proposed algorithm can track human motion at a certain speed and precision. © 2019 IEEE.

关键词： Mobile robots

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Incentivizing Strategy for Demand Response Aggregator Considering Market Entry Criterion: A Game Theoretical Approach

Incentivizing Strategy for Demand Response Aggregator Consid...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Mengmeng Yu Seung Ho Hong Xiongfeng Zhang Junhui Jiang Xuefei Huang Min Wei Kai Wang Wei Liang Department of Electronic Engineering Hanyang University Ansan Korea National Industrial IoT International Cooperation Base Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

This paper presents a novel incentivizing strategy for a demand response (DR) aggregator to participate in an intra-day market organized by the grid operator (GO). By taking the market entry criterion into consideration, the proposed incentivizing strategy is able to help the aggregator determine the optimal incentive for each customer based on their identified characteristics or preferences. The hierarchical decision making procedure between the aggregator and customers is casted into a Stackelberg game. In accordance, an optimal incentivizing strategy can be ascertained for the aggregator, which further helps the aggregator elicit optimal load reductions from enrolled customers meanwhile maximizing its potential profit. Simulation results show that the proposed incentivizing strategy is effective in helping the aggregator determine optimal incentive for each customer in face of different external factors.

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Broad learning system based on maximum correntropy criterion

arXiv

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arXiv 2019年

作者： Zheng, Yunfei Chen, Badong Wang, Shiyuan Wang, Weiqun Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China College of Electronic and Information Engineering Southwest University Chongqing400715 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

As an effective and efficient discriminative learning method, Broad Learning System (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems. However, the standard BLS is derived under the minimum mean square error (MMSE) criterion, which is, of course, not always a good choice due to its sensitivity to outliers. To enhance the robustness of BLS, we propose in this work to adopt the maximum correntropy criterion (MCC) to train the output weights, obtaining a correntropy based broad learning system (C-BLS). Thanks to the inherent superiorities of MCC, the proposed C-BLS is expected to achieve excellent robustness to outliers while maintaining the original performance of the standard BLS in Gaussian or noise-free environment. In addition, three alternative incremental learning algorithms, derived from a weighted regularized least-squares solution rather than pseudoinverse formula, for C-BLS are developed. With the incremental learning algorithms, the system can be updated quickly without the entire retraining process from the beginning, when some new samples arrive or the network deems to be expanded. Experiments on various regression and classification datasets are reported to demonstrate the desirable performance of the new methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms

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An Abnormal state Forecasting Method for Substation Equipment based on Improved LSTM

An Abnormal State Forecasting Method for Substation Equipmen...

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Sustainable Power and Energy Conference (iSPEC)

作者： Tong Li Shimao Yu Chunhe Song Tongwei Yu Peng Zeng Li Shen Liaoning Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China State Grid Liaoning Electric Power Co. Ltd. Shenyang China

Abnormal state accumulation over a long period will cause an electrical equipment fault. Therefore, Substation equipment state forecasting plays a vital role in smart grids. Fault forecasting method based on deep learning have been put forward in recent years, but they have deficiencies dealing with temporal features. This paper proposes an abnormal forecasting method for substation equipment based on improved LSTM, and temporal features of power equipment fault can be extracted effectively for a long-time span. The method reads secondary equipment information through SCADA system, then extracts feature to carry out deep learning training. In this paper, RNN and LSTM are firstly compared, and then the structure and algorithm of improved LSTM are given. This method increases coupling between forget gate and cell state compared with the standard LSTM structure, so as to make better use of information at previous moment. Meanwhile problems of network training and overfitting are discussed. Experiments are conducted to demonstrate that improved LSTM is capable of forecasting accurately the complex electric equipment state with a long forecasting horizon. The accuracy of abnormal state forecasting can reach more than 93% which has a better performance compared with many other methods.

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Robust converter-fed motor control based on active rejection of multiple disturbancesI

arXiv

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arXiv 2020年

作者： Madonskia, Rafal Lakomy, Krzysztof Stankovic, Momir Shao, Sally Yang, Jun Li, Shihua Energy Electricity Research Center International Energy College Jinan University 206 Qianshan Road ZhuhaiGuangdong519070 China Institute of Automation and Robotics Poznan University of Technology Poznan Poland Military Academy University of Defense Belgrade Serbia Department of Mathematics Cleveland State University ClevelandOH United States School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Nanjing China

In this work, an advanced motion controller is proposed for buck converter-fed DC motor systems. The design is based on an idea of active disturbance rejection control (ADRC) with its key component being a custom observer capable of reconstructing various types of disturbances (including complex, harmonic signals). A special formulation of the proposed design allows the control action to be expressed in a concise and practically appealing form reducing its implementation requirements. The obtained experimental results show increased performance of the introduced approach over conventionally used methods in tracking precision and disturbance rejection, while keeping similar level of energy consumption. A stability analysis using theory of singular perturbation further supports the validity of proposed control approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Disturbance rejection

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Design of Quadrotor control System based on Dynamic Surface Output Regulation

Design of Quadrotor Control System based on Dynamic Surface ...

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第37届中国控制会议

作者： Junfang Li Feng Li Yuehui Ji Qiang Gao Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Tianjin University of Technology Tianjin Key Laboratory of Intelligent Robotics Nankai University

A double-loop control system is proposed based on dynamic surface output regulation and integral sliding mode control method,to achieve the position and the attitude tracking control of the quadrotor with external ***,in the position control,the dynamic surface technology is combined with the internal model to design an output regulator,which can transform the position controller into a position augmented system stabilization *** integral sliding mode control method is adopted in the attitude *** internal model is an important and effective tool in the output regulation theory,which can solve the asymptotic tracking and disturbance rejection problems of uncertain systems,and the dynamic surface method allows the mathematical model of the object to be not differentiable,thus the computational expansion is *** stability of the designed control system is proved by Lyapunov stability theory,simulation results in Matlab/Simulink are presented to verify the effectiveness and robustness of the proposed control system.

关键词： quadrotor output regulation dynamic surface internal model integral sliding mode

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Obstacle avoidance method of robot based on admittance control

Obstacle avoidance method of robot based on admittance contr...

引用

2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Hu, Hongshuang Jiang, Yong Tan, Xiaowei School of Information and Control Engineering Shenyang Jianzhu University Shenyang Liaoning Province China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning Province China

ISBN: (纸本)9781538680698

Instead of traditional ranging radar and camera, Kinect was used to ensure the robot can avoid obstacles in the process of movement. And an obstacle avoidance method of robot was proposed based on admittance control. The relative distance between the robot and human hand was real-time measured by Kinect sensor and converted into a virtual force, acting as an input of the admittance controller to control the next moment speed of the controller to avoid obstacles. Then a modification algorithm for the robot's speed direction was added on the basis of admittance control, when the operator's arm is moving at a high speed, to control the robot's avoid obstacles urgently. At the same time, a method of singular transition to the joint space was proposed to solve the problem that once the robot encountering strange points in Cartesian space it will out of control. Finally, a physical experiment platform based on Kinect sensor and UR robot was set up to conduct dynamic and static obstacle avoidance experiments. The results verified the effectiveness of the proposed method. © 2018 IEEE.

关键词： Human computer interaction

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A Novel Assist-As-Needed controller Based on Fuzzy-Logic Inference and Human Impedance Identification for Upper-Limb Rehabilitation

A Novel Assist-As-Needed Controller Based on Fuzzy-Logic Inf...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Chen Wang Liang Peng Zeng-Guang Hou Weiqun Wang Tingting Su State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Department of Mechanical and Electrical Engineering North China University of Technology Beijing P.R. China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Upper-limb rehabilitation robots are being increasingly included in the rehabilitation after stroke. Clinical studies suggest that the patient's active participation can maximize effectiveness of robot-assisted rehabilitation and result in fastest possible recovery. In order to promote active involvement of the patient, we develop a subject-adaptive controller within the assist-as-needed (AAN) paradigm. The controller employs Recursive Least Square (RLS) algorithm to identify human arm impedance parameters, with aims to quantify the residual motor capability of the patient. According to the upper-limb impedance of the patient in the direction along movements, the reference trajectory can be generated based on the motion patterns in healthy humans. In addition, the fuzzy logic control strategy is implemented in the direction perpendicular to movements, which is capable of adjusting the robotic assistance level by considering both upper-limb impedance of the patient and the variations of deviation between the reference trajectory and actual trajectory. Furthermore, the proposed subject-adaptive AAN controller was validated in the simulation study, and the results demonstrated the capability of the controller to modulate the assistance level in accordance with the subject's instantaneous requirements. Future works will focus on the implementation of the control scheme with post-stroke patients.

关键词： Impedance Trajectory Fuzzy logic Training Rehabilitation robotics Task analysis

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Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series

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Journal of Central South University 2017年第3期24卷 692-698页

作者： ZENG Ming LI Jing-hai MENG Qing-hao ZHANG Xiao-nei Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control （School of Electrical Automation and Information Engineering Tianjin University） Tianjin 300072 China

Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern ***,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than *** indicates that all these wind speed time series have non-stationary ***,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer ***,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.

关键词： temporal-spatial cross-correlation near-surface wind speed time series detrended cross-correlation analysis （DCCA） cross-correlation coefficient Pearson coefficient cross-correlation function

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