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第42届中国控制会议

作者： Kai Gong Yingmin Jia The Seventh Research Division and the Center for Information and Control School of Automation Science and Electrical EngineeringBeihang University (BUAA)

This paper presents a coordinated control scheme for space manipulators. The dynamics of both space manipulators and space-tumbling targets are established. A target reference angular acceleration scheme is designed to facilitate the detumbling controller, ensuring that the space target can be detumbled and remain in the desired attitude. A sliding variable is designed to effectively control the convergence of joint trajectories tracking errors to zero. Furthermore, the coordinated controller is carefully designed for joints trajectories tracking while detumbling the space target. Simulation results demonstrate the efficiency of the proposed coordinated controller scheme in addressing the control problem of space manipulators while successfully detumbling the space target.

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ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Network for Multi-Step Prediction of Wastewater Quality Variables

ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Net...

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Robotics, control and automation engineering (RCAE), International Conference on

作者： Yue Sun Yiqi Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Given the limitations of hardware sensors together with strong nonlinearities and nexus between variables in wastewater treatment processes (WWTPs), the measurement of key quality variables is usually inaccurate or even impossible. Moreover, due to the short data sampling period, the single-output single-step prediction model cannot meet the actual demand. Therefore, this paper proposes a new soft sensor, termed as the Echo State Multi-Scale Convolutional Neural Network (ES-MSCNN), designed for accurate multivariate multi-step prediction. ES-MSCNN first generates an echo state representation (ESR) containing temporal information through an echo state network (ESN). Subsequently, a convolutional neural network (CNN) is employed to avoid the shallowness of ESN and further extract high-dimensional feature information from the data. Besides, a multi-scale learning strategy is used in the convolutional module to capture multi-timescale features and improve the multivariate prediction accuracy. Validation on the benchmark simulation model 2 platform and comparison with several models indicate that the proposed ES-MSCNN has higher accuracy and stronger robustness in multi-step prediction tasks with multi-input and multi-output.

关键词： Accuracy Soft sensors Predictive models Feature extraction Prediction algorithms Robustness Convolutional neural networks Wastewater Wastewater treatment Robots

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Distributed Finite-Time Cooperative Guidance Against a Moving Target: A Finite-Time Observer Approach

Distributed Finite-Time Cooperative Guidance Against a Movin...

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2021 IEEE International Conference on Unmanned Systems, ICUS 2021

作者： Xiaofei, Dong Zhang, Ren School of Automation Science and Electrical Engineering Beihang University Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9780738146577

This paper investigates the cooperative guidance issue for multiple unmanned aerial vehicles (UAVs) for simultaneous attacks on maneuvering targets with a prescribed angle formation. Each UAV is accessible to the relative information with respect to the target while the absolute information of the target is unavailable. Also, each UAV just interacts with its neighbors locally. By introducing the integral sliding mode technique, a finite-time observer for the exact estimation of the absolute information of the target is designed. With the help of such an observer, a distributed cooperative guidance algorithm is developed such that both the time-to-go estimate consensus and angle formation objectives are achieved in finite time given that the undirected graph is connected. Finally, a simulation example is presented to validate the efficiency of the suggested guidance algorithm. © 2021 IEEE.

关键词： Antennas

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Task Offloading and Primary Node Selection in Blockchain, Digital Twin, and MEC Enabled Internet of Vehicles 100

Task Offloading and Primary Node Selection in Blockchain, Di...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Du, Jianbo Fang, Huifang Li, Shulei Du, Pengfei Kong, Ziwen Kang, Jiawen Niyato, Dusit Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an China Xidian University School of Communications Engineering Xi'an710071 China Tianyuan Ruixin Communication Technology Co. LTD. Xi'an710071 China Xihua University Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Chengdu610039 China Guangdong University of Technology School of Automation China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798331517786

In this paper, we investigate the safe task offloading and primary node selection in blockchain, digital twin (DT) and Multi-access Edge Computing (MEC) enabled Internet of Vehicles (IoV). Edge servers centers provide computing power for task processing for Mobile Vehicles (MVs), while blockchain can provide security guarantees for MVs during task offloading. Based on the above system, we propose a joint optimization scheme for vehicle task offloading decision and the Practical Byzantine Fault Tolerance (PBFT) consensus process. Due to the large number of optimization variables and constraints, the problem becomes more complex. Traditional convex optimization and dynamic programming methods are difficult to effectively solve this problem. To address this issue, we propose a deep reinforcement learning based algorithm that utilizes Proximal Policy Optimization (PPO). The experimental results show that the algorithm proposed in this paper outperforms the benchmark algorithm in terms of convergence and other aspects. © 2024 IEEE.

关键词： Dynamic programming

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TensWheBot: A Double Wheeled Rolling Robot Based on Tensegrity Structures

TensWheBot: A Double Wheeled Rolling Robot Based on Tensegri...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Niansong Liu, Gang Li, Yiqing Li, Ke Yu, Zhiyuan Wu, Haiteng Liu, Yixiang Li, Yibin Nanjing University of Science and Technology School of Mechanical Engineering Nanjing210094 China Chengdu Aeronautic Polytechnic School of Automotive Engineering Chengdu610100 China Harbin Institute of Technology School of Mechatronics Engineering and Automation Shenzhen Shenzhen518055 China Beijing Institute of Precision Mechatronics and Controls Laboratory of Aerospace Servo Actuation and Transmission Beijing100076 China Hangzhou Shenhao Technology Co. Ltd Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou311121 China Shandong University School of Control Science and Engineering Jinan250061 China Jilin University Key Laboratory of Bionic Engineering Ministry of Education Jilin130022 China

ISBN: (纸本)9781665481090

Tensegrity structure constitutes a self-supporting and self-stressed spatial grid framework, comprising distinct compression components and continuous tension components, which integrates the benefits of rigid structure and flexible structure. This paper introduces TensWheBot, a robot designed with deformable wheels based on tensegrity structure. Based on the force density method, the form-finding of the tensegrity module is accomplished. The tensegrity module can actively alter its shape through the central driving compression rod, thereby modifying the width and the wheel diameter of the robot. The robot's motion law is derived from kinematic modeling. The assembly of the prototype, the establishment of the robot model, and the results of related simulation experiments have validated the effectiveness of the robot. The tensegrity wheeled robot meets the needs of complex and variable environments, as well as diverse tasks such as disaster rescue and terrain exploration. © 2024 IEEE.

关键词： Wheels

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Competitive Meta-Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1902-1904页

作者： Boxi Weng Jian Sun Gao Huang Fang Deng Gang Wang Jie Chen National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081 Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China School of Automation Tsinghua UniversityBeijing 100084China Department of Control Science and Engineering Tongji UniversityShanghai 201804 National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China IEEE

Dear Editor,This letter is concerned with developing meta-learning models for fast,stable,and effective few-shot learning across tasks over a few training ***,deep and reinforcement learning(RL)is widely used in auton... 详细信息

关键词： robot autonomous concerned

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GPIO Based Iterative Learning control for Manipulators with Time-Varying Disturbances 42

GPIO Based Iterative Learning Control for Manipulators with ...

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42nd Chinese control Conference, CCC 2023

作者： Lu, Huangyuwei Wang, Zuo Sun, Jiankun Xiao, Xi Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518063 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing211106 China

ISBN: (纸本)9789887581543

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators, a composite iterative learning control (ILC) scheme via generalized proportional integral observer (GPIO) is proposed. A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics, parameter uncertainties and load fluctuations. A GPIO is then designed to estimate the disturbances and high-order time derivatives. By introducing estimations into a PD-type ILC at each iteration, a composite control scheme is obtained, such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of manipulators. In this paper, the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to infinity. The effectiveness of the proposed control scheme is finally verified by simulation results. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Disturbance rejection

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LQ Optimal control of First-Order Hyperbolic PDE Systems with Final State Constraints

arXiv

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arXiv 2024年

作者： Xue, Xiaomin Xu, Juanjuan Zhang, Huanshui Hu, Long The School of Control Science and Engineering Shandong University Jinan250061 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266000 China School of Mathematics Shandong University Jinan250100 China

This paper studies the linear-quadratic (LQ) optimal control problem of a class of systems governed by the first-order hyperbolic partial differential equations (PDEs) with final state constraints. The main contribution is to present the solvability condition and the corresponding explicit optimal controller by using the Lagrange multiplier method and the technique of solving forward and backward partial differential equations (FBPDEs). In particular, the result is reduced to the case with zero-valued final state constraints. Several numerical examples are provided to demonstrate the performance of the designed optimal controller. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Effect of stagger angle on capillary performance of microgroove structures with reentrant cavities

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science China(Technological sciences) 2021年第7期64卷 1436-1446页

作者： SUN YaLong LIANG FuYe TANG Yong TANG Heng XI XiaoQian YANG Shu FU Ting Intelligent Manufacturing Engineering Laboratory of Functional Structure and Device in Guangdong School of Mechanical and Automotive EngineeringSouth China University of TechnologyGuangzhou510640China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen518000China School of Machinery and Automation Wuhan University of Science and TechnologyWuhan430000China

Aluminum-based microgroove surfaces with reentrant cavities (MSRCs) were fabricated by two staggered ploughing/extrusion processes to meet the requirements of lightweight phase change heat transfer *** MSRCs with different stagger angles between cavities and microgrooves (MGs) were fabricated to study the effect of stagger angle on capillary *** rise and permeability tests were performed on all MSRCs and the results were compared with MGs having the same processing *** was found that MSRCs with smaller stagger angles have higher capillary height,and the maximum enhancement maintained by MSRC45 was about 54.84%.However,MSRCs with larger stagger angles were found to have higher ***,the capillary parameter K·ΔP_(cap)was used as a comprehensive index to evaluate these ***90 and MSRC75 obtained the largest K·ΔP_(cap)values without and with the effect of gravity considered,*** all MSRCs had a higher capillary rise height than MGs,smaller stagger angles (≤60°) seriously reduced the permeability of MSRCs and even resulted in smaller K·ΔP_(cap)value than that of MGs when calculated considering the effect of ***,MSRCs with larger stagger angles (≥75°) may be the optimum wicks due to the good balance between capillary pressure and permeability.

关键词： stagger angle capillary performance microgroove structure reentrant cavities

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Distributed Privacy-Preserving State-of-Charge Balance control for Multiple Energy Storage Systems in Smart Grid

Distributed Privacy-Preserving State-of-Charge Balance Contr...

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Chinese automation Congress (CAC)

作者： Weijia Liu Fanghong Guo Lantao Xing Hongye Su Lei Wang College of Control Science and Engineering Zhejiang University Hangzhou China Department of Automation Zhejiang University of Technology Hangzhou China School of Control Science and Engineering Shandong University Jinan China

This paper studies the distributed state of charge (SoC) balance control strategy in battery energy storage systems (BESSs), satisfying privacy-preserving requirement of BESSs privacy-sensitive data, i.e., configurations and SoC, which may be transmitted among BESSs. Considering each BESS communicates with its neighboring BESS, we use a distributed dynamic average consensus algorithm to do parameter estimations. Using obtained parameters, we set proper power output of each BESS and maintains overall maximum capability. Different from common algorithms, this algorithm not only converges to the exact average of reference signals and thus help set control power outputs, but also ensures the desired differential privacy by adding zero mean Laplace noises, thus protecting privacy-sensitive data from possible adversaries. Effectiveness of the presented approach is demonstrated by simulations.

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