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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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Security of Networked control systems with Incomplete Information Based on Game Theory

Security of Networked Control Systems with Incomplete Inform...

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第三十九届中国控制会议

作者： Lei Gao Jian Sun Jun Li Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center China Industrial Control Systems Cyber Emergency Response Team

The security problem of networked control systems（NCSs） suffering denial of service（DoS） attacks with incomplete information is investigated in this paper. Data transmission among different components in NCSs may be blocked due to DoS attacks. We use the concept of security level to describe the degree of security of different components in an NCS. Intrusion detection system（IDS） is used to monitor the invalid data generated by DoS attacks. At each time slot, the defender considers which component to monitor while the attacker considers which place for invasion. A one–shot game between attacker and defender is built and both the complete information case and the incomplete information case are considered. Furthermore, a repeated game model with updating beliefs is also established based on the Bayes’ rule. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

关键词： Networked control systems Security DoS attacks Game theory Incomplete information

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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 labeled training data is available,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 labeled *** computer graphics technology to generate a large number of labeled 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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Real-time terrain assessment and Bayesian-based path planning for off-road navigation

Real-time terrain assessment and Bayesian-based path plannin...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Tianwei Niu Shuwei Yu Liang Wang Haoyu Yuan Shoukun Wang Junzheng Wang Localization and Motion Planning School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the context of unstructured and unknown environment, the autonomous navigation still faces many challenges, such as assessing rough terrain and deciding how to safely navigate complex terrain. In this work, we propose a robust and practical off-road navigation framework that has been successfully deployed on a vibroseis truck for land exploration. First, in degraded wild scenes, a tightly coupled lidar-GNSS-inertial fusion odometry and mapping framework is adopted to construct a local point cloud map around the vehicle in real-time and provide precise localization. Then, based on amplitude-frequency characteristic analysis and point cloud PCA, a multi-layer terrain assessment map containing terrain roughness, obstacles and slope information is obtained. Finally, combining Gaussian distribution based adaptive sampler and Bayesian sequentially updated proposal distribution, a local graph is efficiently built to obtain multiple path solutions under constrained conditions. Both simulations and field experiments show that the proposed navigation framework can decide how to travel on a flat road even in harsh terrain conditions, naturally suppressing frequent attitude angle changes and preventing vehicle accidents.

关键词： Point cloud compression Location awareness Adaptation models Navigation Roads Gaussian distribution Real-time systems Bayes methods Proposals Principal component analysis

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Luo, Jialiang Chen, Shichao Lv, Yisheng Sun, Wenqiao Yang, Fan Xu, Ruijie Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University The State Key Laboratory for Management and Control of Complex System Jinan China The Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350312492

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field. © 2023 IEEE.

关键词： Cost effectiveness

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