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第29届中国控制与决策会议

作者： Shuai Li Jian Li Siqi Li Guodong Li Yu Mu The Bionic Robot and System Key Laboratory School of Mechatronical EngineeringBeijing Institute of Technology Key Laboratory for Intelligent Control and Decision on Complex Systems School of AutomationBeijing Institute of Technology

ISBN: (纸本)9781509046584

Stroke is mainly caused by the brain damage due to the acute cerebral vascular disease and its primary disease,which can make upper limb or lower limb on one side of the body *** rehabilitation training is the most effective way to regain the Activities of Daily Living(ADL).Based on the concept of humanoid and modular design,this paper outlines the design process of humanoid ***,kinematics and dynamics are calculated in different coordinate frames,which makes simulation and control algorithms are more complex and not intuitive,for the forked-tree structure rehabilitation robot,a modified geometric description method of DH parameters is proposed to solve the problem of inconsistent frames,the whole body Jacobian matrix of this structure is derived and used for the inverse kinematics,and the singular configurations of redundant manipulator are *** important design criteria of humanoid robot is discussed,and the realization of the mechanical design is ***,the experimental results show that the humanoid robot can effectively carry out multiple parts rehabilitation training.

关键词： Rehabilitation Robot Humanoid robot Modularity Singularity Geometric Representation

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Autonomous Tracking control for Four-Wheel Independent Steering Robot Based on Improved Pure Pursuit

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Journal of Beijing Institute of Technology 2020年第4期29卷 466-473页

作者： Jing Li Qingbin Wu Junzheng Wang Hui Qin Jiehao Li Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of TechnologyBeijing 100081China

Autonomous tracking control is one of the fundamental challenges in the field of robotic autonomous navigation,especially for future intelligent *** this paper,an improved pure pursuit control method is proposed for the path tracking control problem of a four-wheel independent steering *** on the analysis of the four-wheel independent steering model,the kinematic model and the steering geometry model of the robot are *** the path tracking control is realized by considering the correlation between the look-ahead distance and the velocity,as well as the lateral error between the robot and the reference *** experimental results demonstrate that the improved pure pursuit control method has the advantages of small steady-state error,fast response and strong robustness,which can effectively improve the accuracy of path tracking.

关键词： autonomous tracking control four-wheel independent steering robot pure pursuit lateral error

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Social Media Based Transportation Research: the state of the Work and the Networking

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 19-26页

作者： Yisheng Lv Yuanyuan Chen Xiqiao Zhang Yanjie Duan Naiqiang Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries Department of Transportation Engineering School of Transportation Science and Technology Harbin Institute of Technology

Recently, there has been an increased interest in the use of social media data as important traffic information sources. In this paper, we review social media based transportation research with social network analysis *** summarize main research topics in this field, and report collaboration patterns at levels of researchers, institutions, and countries, respectively. Finally, some future research directions are identified. © 2014 Chinese Association of Automation.

关键词： Traffic control Transportation

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Parallel control for Optimal Tracking via Adaptive Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2020年第6期7卷 1662-1674页

作者： Jingwei Lu Qinglai Wei Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078 Qingdao Academy of Intelligent Industries Qingdao 266109China

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear *** existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback ***,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied *** address this problem,an augmented system and an augmented performance index function are proposed ***,the general nonlinear system is transformed into an affine nonlinear *** difference between the optimal parallel control and the optimal state feedback control is analyzed *** is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index ***,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function *** stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference ***,the effectiveness of the developed optimal parallel control method is verified in two cases.

关键词： Adaptive dynamic programming(ADP) nonlinear optimal control parallel controller parallel control theory parallel system tracking control neural network(NN)

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Stabilization of Perturbed Continuous-Time systems Using Event-Triggered Model Predictive control

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IEEE Transactions on Cybernetics 2022年第5期52卷 4039-4051页

作者： Wang, Mengzhi Sun, Jian Chen, Jie Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex System Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

In this article, event-triggered model predictive control (EMPC) of continuous-time nonlinear systems with bounded disturbances is studied. Two novel event-triggered control schemes are proposed. In the first strategy, an event-triggering condition, designed based on the state error between the actual system state and the optimal one, with an absolute threshold is considered. In the second strategy, an event-triggering condition with a mixed threshold is designed to further save the computational resources. The minimal interevent times of both event-triggered control schemes are obtained to avoid the Zeno behavior. Sufficient conditions of recursive feasibility for these two triggering strategies, which refer to the prediction horizon, the triggering level, and the disturbance bound, are obtained, respectively. Input-to-state practical stability (ISpS) of both event-triggered control systems is established without requiring the system state entering the terminal set in finite time, respectively. Finally, the numerical simulation shows the effectiveness of the proposed methods. © 2013 IEEE.

关键词： Model predictive control

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A CNN-ELM Compound Fault Diagnosis Method Based on Joint Distribution Modification 7

A CNN-ELM Compound Fault Diagnosis Method Based on Joint Dis...

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7th International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2020

作者： Dong, Jiechao Yan, Liping Xia, Yuanqing Zhang, Jinhui Wang, Xianghua Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems Shandong266590 China

ISBN: (纸本)9781728162461

In the industrial field, compound faults often occur on rolling bearings and it's difficult to diagnose them correctly. To solve this problem, this article proposes a CNN-ELM compound fault diagnosis method based on joint distribution modification. Firstly, considering the complementarity and coupling of data from multiple sensors, a data input trick of multi-sensor data connected in parallel is designed. Secondly, due to the discrepancy of distribution between the compound fault data features and the single fault data features, the marginal distribution matrix and the posterior distribution matrix are used to modify the CNN-ELM network, so that the network can extract more reliable data features for fault diagnosis. Finally, referring to the categories and criteria of bearing damage proposed by Paderborn University, the label code is defined. The corresponding data set is used to verify the proposed algorithm. Experimental results show that the algorithm can accurately obtain detailed fault information such as fault location, fault type, and fault severity. © 2020 IEEE.

关键词： Roller bearings

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Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

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自动化学报（英文版） 2021年第10期8卷 1657-1666页

作者： Ting Zhang Wenjie Song Mengyin Fu Yi Yang Meiling Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of TechnologyBeijing 100081 Nanjing University of Science and Technology Nanjing 210014China

Intersections are quite important and complex traffic scenarios, where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles. Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined, this paper proposes a long short-term memory based (LSTM-based) framework that combines intention prediction and trajectory prediction together. First, we build an intersection prior trajectories model (IPTM) by clustering and statistically analyzing a large number of prior traffic flow trajectories. The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory, and also serves as a reference for credibility evaluation. Second, we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage. Furthermore, the predicted intention is also a key that is associated with the prior trajectories model. The proposed framework is validated on two publically released datasets, next generation simulation (NGSIM) and INTERACTION. Compared with other prediction methods, our framework is able to sample a trajectory from the estimated distribution, with its accuracy improved by about 20％. Finally, the credibility evaluation, which is based on the prior trajectories model, makes the framework more practical in the real-world applications.

关键词： Autonomous vehicle intersection motion prediction prior trajectories model turning intention

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An efficient decision and planning method for high speed autonomous driving in dynamic environment 28

An efficient decision and planning method for high speed aut...

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28th IEEE intelligent Vehicles Symposium, IV 2017

作者： Zhang, Kai Fu, Mengyin Yang, Yi Shang, Songtian Wang, Meiling State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institution of Technology Beijing 100081 China

ISBN: (纸本)9781509048052

This paper describes an improved decision and planning algorithm based on our previously proposed methods for unmanned ground vehicle (UGV). The new method can be applied to UGV driving both in structured environment and unstructured environment. In the improved method, the prospect of planning is extended from 40m to 100m for safe driving at high speed and some piecewise linear speed functions are designed for the new prospect. After this improvement our UGV now can drive at a maximum speed of 60km/h rather than 40km/h while avoiding obstacles safely. Besides, a velocity feedforward control is added to make the UGV overtake other cars driving at about 25km/h on the road. At last, the collision detection algorithm is improved to make the lane changing maneuver safer. The proposed decision and planning algorithm is implemented both on our old Polaris all terrain vehicle (ATV) and new FAW-H7 car, which exhibited good performance on Across Dangers & Obstacles 2016, Tahe, China and Future Challenge 2016, Changshu, China, respectively. © 2017 IEEE.

关键词： Planning Automobiles Laser radar Roads Feedforward neural networks Collision avoidance Cameras

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Semantic motion segmentation for urban dynamic scene understanding

Semantic motion segmentation for urban dynamic scene underst...

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2016 IEEE International Conference on Automation Science and Engineering, CASE 2016

作者： Fan, Qiu Yi, Yang Hao, Li Mengyin, Fu Shunting, Wang School of Automation State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781509024094

A mount of recent researches on scene parsing and semantic labeling, while few focus on obtaining joint semantic motion labeling. In this paper, we propose an approach to infer both the object class and motion status for each pixel of images. First, we extract and match sparse image features to estimate ego-motion between two consecutive stereo images, the result of feature points grouping is used to segment moving object in U-disparity map. Second, a Fully Convolutional Neural Network is employed for semantic segmentation. Moreover, semantic cues are utilized to remove pixels have no potential to be moved in motion mask. Finally, we use a fully connected CRF to integrate motion into semantic segmentation. To validate the effectiveness of the proposed algorithm, we present experimental results with KITTI stereo images that contain moving objects. © 2016 IEEE.

关键词： Semantics

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UKF based Fault Detection and state Estimation for Nonlinear systems with Correlated Noise 36

UKF based Fault Detection and State Estimation for Nonlinear...

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第36届中国控制会议

作者： Hongxue Chen Liping Yan Yuanqing Xia Mengyin Fu Bo Xiao Guangzheng Hu Huifang Li Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781538629185

state estimation and fault diagnosis are essential topics for dynamic *** Kalman fllter（UKF） has been widely applied in nonlinear *** classical UKF algorithm is built on the premise that process noise and measurement noise is *** practical problems,this assumption is not always *** addition,due to the limitation of communication and sensor fault,etc.,data missing or unreliable measurements will happen ***,it is very important to study the state estimation of nonlinear systems with unreliable measurements and correlated *** this paper,an UKF based state estimation algorithm with unreliable observations under correlated noise is presented.A numerical example is given to show the feasibility and effectiveness of the presented algorithm.

关键词： fault detection state estimation correlated noise nonlinear system

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