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arXiv 2021年

作者： Cai, Deheng Liu, Wei Ji, Linong Shi, Dawei State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Department of Endocrine and Metabolism Peking University People’s Hospital Beijing China

Effective postprandial glucose control is important to glucose management for subjects with diabetes mellitus. In this work, a data-driven meal bolus decision method is proposed without the need of subject-specific glucose management parameters. The postprandial glucose dynamics is learnt using Gaussian process regression. Considering the asymmetric risks of hyper- and hypoglycemia and the uncertainties in the predicted glucose trajectories, an asymmetric risk-sensitive cost function is designed. Bayesian optimization is utilized to solve the optimization problem, since the gradient of the cost function is unavailable. The proposed approach is evaluated using the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the standard insulin bolus calculator. For the case of announced meals, the proposed method achieves satisfactory and similar performance in terms of mean glucose and percentage time in [70, 180] mg/dL without increasing the risk of hypoglycemia. Similar results are observed for the case without the meal information (assuming that the patient follows a consistent diet) and the case of basal rate mismatches. In addition, advisory-mode analysis is performed based on clinical data, which indicates that the method can determine safe and reasonable meal boluses in real clinical settings. The results verify the effectiveness and robustness of the proposed method and indicate the feasibility of achieving improved postprandial glucose regulation through a data-driven optimal control method. © 2021, CC BY.

关键词： Gaussian distribution

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Tracking Performance of Networked systems with Limited Bandwidth and Colored Noise

Tracking Performance of Networked Systems with Limited Bandw...

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Chinese Automation Congress (CAC)

作者： Bin Zhang Xiaowei Jiang Li Sun Xianhe Zhang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

This study investigated the optimal tracking performance (OTP) of multi-input multi-output (MIMO), discrete- time networked control systems (NCSs). The limits of tracking performance (TP) under the influences of bandwidth, encoding- decoding, and additive coloured Gaussian noise (ACGN) are derived using the techniques of coprime decomposition and all-pass decomposition. The results reveal the negative impact of non-minimum phase (NMP) zeros and unstable poles of the plant as well as network communication constraints on the TP of NCSs. Finally, a numerical simulation is discussed and verifies our conclusions.

关键词： Networked control systems Gaussian noise Bandwidth Numerical simulation Downlink Decoding Colored noise

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Soft-Landing Dynamics of a Type of Four-Legged Space Lander

SSRN

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SSRN 2024年

作者： Shi, Tengfei Shi, Tengfei Yang, Yinping Zhang, Zhijuan Liu, Cuicui Liu, Caishan Ma, Daolin Applied Mechanics and Structure Safety Key Laboratory of Sichuan Province School of Mechanics and Aerospace Engineering Southwest Jiaotong University Chengdu610031 China State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China Institute of Dynamics and Control College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology Beijing100871 China China Academy of Space Technology Beijing100094 China School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System Ministry of Education Shanghai200240 China

Analyzing the landing dynamics response is essential for enhancing the success rate of surface explorations on Mars, the moon, or asteroids. This paper develops a comprehensive dynamics model for a space lander equipped with four sets of buffer legs, each consisting of a primary strut, two secondary struts, and a footpad, during its soft landing process. The degrees of freedom of the main body and the footpads are defined as generalized coordinates to describe the motion of the space lander. The compression force of the buffer struts is obtained by interpolating real experimental data, and the contact force between the footpad and the ground is calculated using Archimedes law for granular media. Then, the dynamical model for the lander in terms of minimal degrees of freedom can be constructed by applying the Lagrange equations of the second kind. The proposed dynamics model is validated by the experiments of a single-legged lander vertical touchdown and a four-legged lander free touchdown. Moreover, histories of buffer force, acceleration of the main body, and other key physical quantities are computed to investigate the landing process. This highly efficient and accurate dynamics model provides a method to perform extensive simulations to design and optimize a soft landing process. © 2024, The Authors. All rights reserved.

关键词： Landing

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Trajectory Tracking Strategy for Gliding Hypersonic Vehicle with Aileron Stuck at an Unknown Angle

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Journal of Beijing Institute of Technology 2019年第3期28卷 447-455页

作者： Haibo Ji Lei Wang School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Ministry of EducationBeijing 100081China

A nonlinear robust trajectory tracking strategy for a gliding hypersonic vehicle with an aileron stuck at an unknown position is presented in this paper. First, the components of translational motion dynamics perpendicular to the velocity are derived, and then a guidance law based on a time-varying sliding mode method is used to realize trajectory tracking. Furthermore, the rotational equations of motion are separated into an actuated subsystem and an unactuated subsystem. And an adaptive time-varying sliding mode attitude controller is proposed based on the actuated subsystem to track the command attitude and the tracking performance and robustness are therefore enhanced. The proposed guidance law and attitude controller make the hypersonic vehicle fly along the reference trajectory even when the aileron is stuck at an unknown angle. Finally, a hypersonic benchmark platform is used to demonstrate the effectiveness of the proposed strategy.

关键词： trajectory tracking stuck aileron time-varying sliding mode

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Behavior-based cooperative control method for fixed-wing UAV swarm through a virtual tube considering safety constraints

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Chinese Journal of Aeronautics 2025年

作者： Siyi YUE Duo ZHENG Mingjun WEI Zhichen CHU Defu LIN School of Astronautics Engineering Beijing Institute of Technology Beijing 100081 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Yungang Street Beijing 100074 China

Unmanned Aerial Vehicle (UAV) swarm collaboration enhances mission effectiveness. However, fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments. Aimed at the cooperative control problem of fixed-wing UAV swarm flights under the airspace constraints of a virtual tube in a complex environment, this paper proposes a behavior-based distributed control method for fixed-wing UAV swarm considering flight safety constraints. Considering the fixed-wing UAV swarm flight problem in complex environment, a virtual tube model based on generator curve is established. The tube keeping, centerline tracking and flight safety behavioral control strategies of the UAV swarm are designed to ensure that the UAV swarm flies along the inside of the virtual tube safety and does not go beyond its boundary. On this basis, a maneuvering decision-making method based on behavioral fusion is proposed to ensure the safe flight of UAV swarm in the restricted airspace. This cooperative control method eliminates the need for respective pre-planned trajectories, reduces communication requirements, and achieves a high level of intelligence. Simulation results show that the proposed behavior-based UAV swarm cooperative control method is able to make the fixed-wing UAV swarm, which is faster and unable to hover, fly along the virtual tube airspace under various virtual tube shapes and different swarm sizes, and the spacing between the UAVs is larger than the minimum safe distance during the flight.

关键词： Unmanned aerial vehicles (UAV) UAV swarm Distributed cooperative control Swarm flight safety Behavior-based method Virtual tube airspace

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Motion Planning of Six-DOF Arm Robot Based on Improved DDPG Algorithm

Motion Planning of Six-DOF Arm Robot Based on Improved DDPG ...

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第三十九届中国控制会议

作者： Zhuang Li Hongbin Ma Yazhe Ding Chen Wang Ying Jin School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

This paper presents an improved deep deterministic policy gradient algorithm based on a six-DOF(six multi-degree-offreedom) arm robot. First, we build a robot model based on the DH(Denavit-Hartenberg) parameters of the UR5 arm robot. Then,we improved the experience pool of the traditional DDPG(deep deterministic policy gradient) algorithm by adding a success experience pool and a collision experience pool. Next, the reward function is improved to increase the degree of successful reward and the penalty of collision. Finally, the training is divided into segments, the front three axes are trained first, and then the six axes. The simulation results in ROS(Robot Operating system) show that the improved DDPG algorithm can effectively solve the problem that the six-DOF arm robot moves too far in the configuration space. The trained model can reach the target area in five steps. Compared with the traditional DDPG algorithm, the improved DDPG algorithm has fewer training episodes,but achieves better results.

关键词： Motion Planning Improved DDPG Six-DOF Arm Robot ROS

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Analytic Hierarchy Process（AHP） in Portfolio Selection Based on Information Granularity

Analytic Hierarchy Process（AHP） in Portfolio Selection Bas...

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第32届中国控制与决策会议

作者： Kaixin Zhao Yaping Dai Ye Ji Jiayi Sun School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Analytic Hierarchy Process（AHP） is a multi criteria decision-making method,which can describe and transform the qualitative problems quantitatively,and then get the quantitative analysis results in accordance with the causal relationship between decision *** this paper,a granular Analytic Hierarchy Process,which introduces the granularity mechanism,is proposed to solve the portfolio selection problem under the mean-risk *** the proposed method,the scale value of scheme layer is no longer limited to nine positive integers from 1 to 9,which gives granularity attributes to the comparison of advantages and disadvantages in a specific criterion layer between different *** proposed method reflects small differences between different alternative schemes through granularity attribute,so it can provide rich decision information for decision *** numeric examples from the real-world financial market（China Shanghai Stock Exchange） are provided to illustrate an essence of the proposed method.

关键词： Analytic Hierarchy Process（AHP） Portfolio Selection Information Granularity decision-making

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Fractional-order Virtual Model control for Trotting Motion of Quadruped Robot

Fractional-order Virtual Model Control for Trotting Motion o...

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第32届中国控制与决策会议

作者： JiangBo Zhao Shicheng Ma Shanshuai Niu Junzheng Wang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology

Virtual model control(VMC) is the most widely-used method for motion control of quadruped *** could decrease the abrupt ground reaction force and realize compliant interaction between foot and ground at the cost of control *** improve the robustness and precision of VMC,we integrate a fractional-order virtual damper with the classical VMC and propose a fractional-order VMC(FOVMC) for quadruped robot trotting *** we provide the implementation details about optimization-based FOVMC parameter tuning method and carry out the trotting experiments on the quadruped robot *** experiment results demonstrate that FOVMC could improve the precision of single-leg motion control and achieve a better whole-body trotting performance than classical VMC.

关键词： Fractional-order calculus Virtual model control Quadruped robot Trotting

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DBFCM: a Density-Based Fuzzy C-Means with Self-Regulated Fuzzy Clustering Parameters

DBFCM: a Density-Based Fuzzy C-Means with Self-Regulated Fuz...

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第三十九届中国控制会议

作者： Jiayi Sun Yaping Dai Kaixin Zhao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

As an interdisciplinary of fuzzy theory and clustering, Fuzzy C-Means（FCM） is widely applied for identifying categories with unlabeled data. However, its application to data which is hard to visualize rises the difficulty for users to determine the input parameters, especially for the number of clusters. In this paper, a kind of fuzzy clustering algorithm with self-regulated parameters named Density-Based Fuzzy C-Means（DBFCM） is proposed by integrating the idea of Density-Based Spatial Clustering of Application with Noise（DBSCAN） into FCM. Its advantage is using the inherit density characteristic of input data to self-determine the parameters of fuzzy clustering. The experimental results demonstrate that the proposed DBFCM can not only self-determine the proper parameters, but also accelerate the convergence process compared to the original FCM.

关键词： Machine Learning Fuzzy Clustering Fuzzy C-Means Parameter Self-Regulation

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S$^{\text{4}}$TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2023年第2期9卷 3220-3231页

作者： Xiao Wang Ke Tang Xingyuan Dai Jintao Xu Quancheng Du Rui Ai Yuxiao Wang Weihao Gu Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Haomo Technology Company Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-decision of 94.75%.

关键词： Trajectory Safety Vehicles Trajectory planning Autonomous vehicles Risk management Behavioral sciences

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