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Chinese Automation Congress (CAC)

作者： Yongkang Xu Shoukun Wang Hongqiang Zhao Zhihua Chen State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China State Key Laboratory of High Performance and Complex Manufacturing Central South University Changsha China

ISBN: (纸本)9781665426480

A speed consensus control strategy for a six-wheel-drive skid-steering mobile robot was proposed based on a hierarchical controller in a complex environment with unknown disturbances. The effective method of reliable speed consensus control strategy should consider some disturbances, such as the internal-robot assembly error, which including instability of motor control system and external physical interference in robot dynamic system. In this paper, a driving anti-slip control system with PID and the stability control system with sliding mode by variable structure is designed and integrated to elevate the stability of the robot. And, a G-vector control algorithm based on sliding mode control is proposed to reduce the yaw angle as much as possible during the process of differential steering of robots. The effectiveness of the proposed control strategy is verified by an example of a robot. The results show that the method has good control performance in control accuracy and stability.

关键词： Uncertainty Robot kinematics Heuristic algorithms Velocity control Torque control Wheels Consensus control

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Electric Dual Variable Pump controlled Hydraulic Motor

Electric Dual Variable Pump Controlled Hydraulic Motor

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Chinese control Conference (CCC)

作者： Jianmin Yang Junzheng Wang Shanshuai Niu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P. R. China

The combined control of variable speed and variable displacement is a new type of volume control with high efficiency and fast response. However, due to the inherent nonlinearity of multiplication, it brings certain difficulties to the control. The electric double-variable pump[1] is a dual-input single-output system, and it is a nonlinear system[2]. It is necessary to linearize the system or use a nonlinear control method to control and solve the control problem of the system. In this paper, an intelligent control rule is proposed for the nonlinear problem of double input and single output. Through backstepping design[3], the nonlinear system is transformed into multiple linear subsystems. Then the original system is turned into two independent subsystems with single input and single output, which are controlled separately. The co-simulation platform based on AMESIM and Simulink[4] has been verified and compared with a single PID control algorithm to simulate the step response and sinusoidal tracking performance of the system. The results show that the response speed of the system has been greatly improved.

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Ensemble of Extreme Learning Machines for Regression

Ensemble of Extreme Learning Machines for Regression

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Data Driven control and Learning systems (DDCLS)

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology China

ISBN: (纸本)9781538626191

Regression, as a particular task of machine learning, performs a vital part in data-driven modeling, by finding the connections between the system state variables without any explicit knowledge about the system, using a collection of input-output data. To enhance the prediction performance and maximize the training speed, we propose a fully learnable ensemble of Extreme Learning Machines (ELMs) for regression. The developed approach learns the combination of different individual models, using the ELM algorithm, which is applied to minimize both the prediction error and the norm of the network parameters, which leads to higher generalization performance under Bartlett's theory. Moreover, the average based ELM ensemble may be viewed as a particular case of our model. Extensive experiments on many standard regression benchmark datasets have been carried out, and comparison with different models has been performed. The experimental findings confirm that the proposed ensemble can reach competitive results in term of the generalization performance, and the training speed. Furthermore, the influence of different hyper parameters on the performance, in term of the prediction error and the training speed, of the developed model has been investigated to provide a meaningful guideline to practical applications.

关键词： Computational modeling Training Mathematical model Predictive models Neural networks Prediction algorithms Approximation algorithms

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Action Recognition Algorithm based on 2D Human Pose Estimation Method

Action Recognition Algorithm based on 2D Human Pose Estimati...

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第40届中国控制会议

作者： Chongkai Yu Wenjie Chen Ye Li Chen Chen State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center

This paper presents an action recognition method based on 2D human body node data in video. This method uses the pose estimation algorithm to detect the human body node data in each frame of video information. We get the two-dimensional coordinates and confidence data of the nodes, and optimize the arrangement of these data into a 3D array form similar to ***, we use the classical two-dimensional convolutional neural network to carry out classification training. The test on UCF-101 data set shows that this method can indeed improve the accuracy of action recognition based on RGB information to a certain extent, and reduce the training cost.

关键词： Action recognition Pose estimation Two-dimensional convolutional neural network

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An Introduction to Domain Adaptive Object Detection from Synthesis to Reality

An Introduction to Domain Adaptive Object Detection from Syn...

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第40届中国控制会议

作者： Zhijun Xue Wenjie Chen Jing Li State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center

In recent years,deep learning based object detection has achieved great *** methods typically assume that large amount of lab.led training data is availab.e,meanwhile,training and test data are independent and identically ***,the two assumptions are not always hold in *** many applications,it is prohibitively expensive and time-consuming to obtain large quantities of lab.led *** computer graphics technology to generate a large number of lab.led data provides a solution to this ***,direct transfer across domains from synthesis to reality often performs poorly due to the presence of domain *** adaptive object detection are concerned with accounting for these types of *** this paper,we present an introduction to these ***,we briefly introduce the object detection and domain ***,the synthetic object detection datasets and related software tools are ***,we present a categorization of approaches,divided into discrepancy-based methods,adversarial discriminative methods,reconstruction-based methods and ***,we also discuss some potential deficiencies of current methods and several open problems which can be explored in future work.

关键词： Deep learning Object detection Synthesis to reality Domain adaptation

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On Finite-Horizon Optimal control of First-Order Plus Time Delay systems

On Finite-Horizon Optimal Control of First-Order Plus Time D...

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第27届中国控制与决策会议

作者： Dawei Shi Tongwen Chen State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Department of Electrical and Computer Engineering University of Alberta

In this work,a finite-horizon optimal control problem for first-order plus time delay(FOPTD) processes is *** show that if the control horizon is greater than three and the prediction horizon is great than the control horizon plus the time delay in discrete time,the optimal controller is not affected by either of the two ***,under these conditions,the controller parameters are explicitly calculated,the closed-loop system is shown to be stable,and the controller is *** problem considered is related to the results on linear quadratic regulation of linear systems with time delays;however,the detailed parameterization of the state-space model introduced by the FOPTD process provides an additional opportunity to investigate the exact controller structure and properties(e.g.,the locations of the closed-loop poles),which are also the major difficulties encountered and overcome in this *** problem is motivated from phenomena experienced in designing industrial model predictive control(MPC) tuning algorithms,and extensive numerical examples indicate that the proposed results speed up the MPC autotuning algorithms by 70%.

关键词： Optimal control First-order plus time delay process Performance visualization MPC tuning

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A Unified Framework Integrating decision Making and Trajectory Planning Based on Spatio-Temporal Voxels for Highway Autonomous Driving

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IEEE Transactions on intelligent Transportation systems 2022年第8期23卷 10365-10379页

作者： Zhang, Ting Song, Wenjie Fu, Mengyin Yang, Yi Tian, Xiaohui Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China Nanjing University of Science and Technology School of Automation Nanjing210014 China

intelligent decision making and efficient trajectory planning are closely related in autonomous driving technology, especially in highway environment full of dynamic interactive traffic participants. This work integrates them into a unified hierarchical framework with long-term behavior planning (LTBP) and short-term dynamic planning (STDP) running in two parallel threads with different horizon, consequently forming a closed-loop maneuver and trajectory planning system that can react to the dynamic environment effectively and efficiently. In LTBP, a novel voxel structure and the 'voxel expansion' algorithm are proposed for the generation of driving corridors in 3D configuration, which involves the prediction states of surrounding vehicles. By using Dijkstra search, the maneuver with minimal cost is determined in form of voxel sequences, then a quadratic programming (QP) problem is constructed for solving the optimal trajectory. And in STDP, another small-scaled QP problem is performed to track or adjust the reference trajectory from LTBP in response to the dynamic obstacles. Meanwhile, a Responsibility-Sensitive Safety (RSS) Checker keeps running at high frequency for real-time feedback to ensure security. Experiments on real data collected in different highway scenarios demonstrate the effectiveness and efficiency of our work. © 2000-2011 IEEE.

关键词： decision making

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Model Compression Based on YOLOv3 Object Detection Algorithm from the Perspective of UAV

Model Compression Based on YOLOv3 Object Detection Algorithm...

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第40届中国控制会议

作者： Bing Wu Zhijun Xue Wenjie Chen State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center

Recently,unmanned aerial vehicles are widely used in surveillance,aerial photography,power grid line inspections and other *** order to deploy the YOLOv3[1] algorithm on drones,it is necessary to adopt the YOLOv3 algorithm with fewer parameters and a simpler *** paper implements the model compression of YOLOv3 based on methods such as sparseness,pruning,and knowledge *** paper implements the sparseness of the network by adding L1 regular expressions on the convolutional *** that,redundant channels and layers are removed through channel pruning and layer *** sparse and pruning,the mAP lost a *** using knowledge distillation after pruning,it attempts to recover mAP lost in sparseness and *** this method,the YOLOv3 algorithm can be deployed on embedded platforms such as RK3399 *** evaluate the model on the visdrone2019 *** experimental results show that after model compression,YOLOv3 is more suitable for deployment on embedded platforms.

关键词： Object Detection Model Compress RK3399pro YOLOv3

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Adaptive control of Discrete-time Nonlinear systems Using ITF-ORVFL

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 556-563页

作者： Xiaofei Zhang Hongbin Ma Wenchao Zuo Man Luo School of Automation Beijing Institute of TechnologyBeijing 100081 School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China School of Automation Beijing Institute of Technologyand also with the State Key Laboratory of Intelligent Control and Decision of Complex Systems(Beijing Institute of Technology)Beijing 100081China School of Automation Beijing Institute of TechnologyBeijing 100081and he is with Beijing Institute of Electronic System EngineeringBeijing 100854China School of Automation Beijing Institute of TechnologyBeijing 100081and she is with Ant GroupBeijing 310013China

Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly *** network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural *** this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training *** link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is *** experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.

关键词： Adaptive control initial-training-free online learning algorithm random vector functional link networks

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Leader-Following Consensus of Discrete-Time Linear Multi-Agent systems with Communication Noises

Leader-Following Consensus of Discrete-Time Linear Multi-Age...

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第三十四届中国控制会议

作者： Yunpeng Wang Long Cheng Hongming Wang Zeng-Guang Hou Min Tan Hongnian Yu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology China North Vehicle Research Institute Faculty of Science and Technology Bournemouth University

ISBN: (纸本)9781467374439

This paper studies the leader-following consensus problem of discrete-time generic linear multi-agent systems. Agents share their states with their neighbors via a noisy communication network. An algorithm is proposed for the leader-following consensus problem where the time-varying gain is employed to attenuate noises. Different from most previous results where all agents have to use the same time-varying gain, each agent can have its own time-varying gain. Sufficient conditions for solving the mean square leader-following consensus problem are obtained: 1) the communication topology graph has a spanning tree;2) the summation of every time-varying gain from zero to infinite is infinite;3) all time-varying gains are infinitesimal of the same order as time goes to infinity;and 4) all roots of a so-called "parameter polynomial" are inside the unit circle. Finally, a simulation example is given to verify the theoretical results.

关键词： Multi-Agent system,Leader-following consensus,Mean square,Communication noise.

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