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arXiv 2020年

作者： Gang, Peng Zezao, Lu Shanliang, Chen Bocheng, Chen Dingxin, He Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

To address the weak observability of monocular visual–inertial odometers on ground-based mobile robots, this paper proposes a monocular inertial SLAM algorithm combined with wheel speed anomaly detection. The algorithm uses a wheel speed odometer pre-integration method to add the wheel speed measurement to the least-squares problem in a tightly coupled manner. For abnormal motion situations, such as skidding and abduction, this paper adopts the Mecanum mobile chassis control method, based on torque control. This method use the motion constraint error to estimate the reliability of the wheel speed measurement. At the same time, in order to prevent incorrect chassis speed measurements from negatively influencing robot pose estimation, this paper uses three methods to detect abnormal chassis movement and analyze chassis movement status in real time. When the chassis movement is determined to be abnormal, the wheel odometer pre-integration measurement of the current frame is removed from the state estimation equation, thereby ensuring the accuracy and robustness of the state estimation. Experimental results show that the accuracy and robustness of the method in this paper are better than those of a monocular visual–inertial odometer. Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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Super Twisting control of Passive Gait Training Exoskeleton Driven by Pneumatic Muscles 30

Super Twisting Control of Passive Gait Training Exoskeleton ...

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30th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2019

作者： Zhang, Mengshi Huang, Jian Cao, Yu Zhang, Hai-Tao Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728126746

Pneumatic Muscles (PMs) - driven exoskeleton has a promising prospect in the field of rehabilitation and assistance, because of the PMs intrinsic features of compliance and high force-to-weight ratio. However, the precise control of PMs-driven exoskeleton still remains a challenging problem due to the hysteresis, time-varying parameters of PMs and complexity of the mechanism. To solve this problem, this paper presents a super twisting controller (STC), essentially a second-order sliding mode control, to realize the human gait tracking control of lower limb exoskeleton. As a result, the disturbances and uncertainties of the system can be handled, and the chattering caused by the traditional sliding mode control (SMC) could be eliminated. The stability of the closed-loop system is ensured according to the Lyapunov theorem. In addition, we conduct experiments in real exoskeleton system and the results illustrate the validity of the super twisting algorithm. Pneumatic Muscles (PMs) - driven exoskeleton has a promising prospect in the field of rehabilitation and assistance, because of the PMs' intrinsic features of compliance and high force-to-weight ratio. However, the precise control of PMs-driven exoskeleton still remains a challenging problem due to the hysteresis, time-varying parameters of PMs and complexity of the mechanism. To solve this problem, this paper presents a super twisting controller (STC), essentially a second-order sliding mode control, to realize the human gait tracking control of lower limb exoskeleton. As a result, the disturbances and uncertainties of the system can be handled, and the chattering caused by the traditional sliding mode control (SMC) could be eliminated. The stability of the closed-loop system is ensured according to the Lyapunov theorem. In addition, we conduct experiments in real exoskeleton system and the results illustrate the validity of the super twisting algorithm.. © 2019 IEEE.

关键词： Sliding mode control

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Unsupervised pool-based active learning for linear regression

arXiv

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arXiv 2020年

作者： Liu, Ziang Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

In many real-world machine learning applications, unlabeled data can be easily obtained, but it is very timeconsuming and/or expensive to label them. So, it is desirable to be able to select the optimal samples to label, so that a good machine learning model can be trained from a minimum amount of labeled data. Active learning (AL) has been widely used for this purpose. However, most existing AL approaches are supervised: they train an initial model from a small amount of labeled samples, query new samples based on the model, and then update the model iteratively. Few of them have considered the completely unsupervised AL problem, i.e., starting from zero, how to optimally select the very first few samples to label, without knowing any label information at all. This problem is very challenging, as no label information can be utilized. This paper studies unsupervised pool-based AL for linear regression problems. We propose a novel AL approach that considers simultaneously the informativeness, representativeness, and diversity, three essential criteria in AL. Extensive experiments on 14 datasets from various application domains, using three different linear regression models (ridge regression, LASSO, and linear support vector regression), demonstrated the effectiveness of our proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear regression

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Configuration optimization of electricity-hydrogen-fuel energy system considering waste heat recovery mechanism

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International Journal of Hydrogen Energy 2025年 148卷

作者： Ziqi Sun Yunfeng Peng Zheng Zhou Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China College of Smart Energy Shanghai Jiao Tong University Shanghai 200240 China

To address the challenges of low energy efficiency and economic viability in the electricity-hydrogen-fuel energy system, this study proposes an innovative green methanol production system incorporating waste heat recovery and configuration optimization. This paper establishes a heat transfer pathway integrating methanol synthesis, thermal storage tank, and electrolyzer, repurposing reaction heat from methanol synthesis to enhance electrolyzer performance. A comprehensive collaborative optimization framework is developed for system capacity planning and operation coordination. Results demonstrate that waste heat recovery strategy can increase daily average hydrogen and methanol production by 3.66 % and 7.14 % while enhancing overall system energy and exergy efficiency by 2.65 % and 2.13 %. Economic analysis reveals that allowing 10 % renewable energy curtailment reduces electrolyzer capacity by 22.35 %, hydrogen and oxygen storage tank capacity by 19.78 % and 16.67 %, achieving a 16.16 % reduction in total system investment cost compared to full renewable energy utilization scenario.

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Finite-size scaling of geometric renormalization flows in complex networks

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Physical Review E 2021年第3期104卷 034304-034304页

作者： Dan Chen Housheng Su Xiaofan Wang Gui-Jun Pan Guanrong Chen School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China Department of Automation Shanghai University Shanghai 200072 China Faculty of Physics and Electronic Science Hubei University Wuhan 430062 China Department of Electronic Engineering City University of Hong Kong 999077 Hong Kong

Some characteristics of complex networks need to be derived from global knowledge of the network topologies, which challenges the practice for studying many large-scale real-world networks. Recently, the geometric renormalization technique has provided a good approximation framework to significantly reduce the size and complexity of a network while retaining its “slow” degrees of freedom. However, due to the finite-size effect of real networks, excessive renormalization iterations will eventually cause these important “slow” degrees of freedom to be filtered out. In this paper, we systematically investigate the finite-size scaling of structural and dynamical observables in geometric renormalization flows of both synthetic and real evolutionary networks. Our results show that these observables can be well characterized by a certain scaling function. Specifically, we show that the critical exponent implied by the scaling function is independent of these observables but depends only on the structural properties of the network. To a certain extent, the results of this paper are of great significance for predicting the observable quantities of large-scale real systems and further suggest that the potential scale invariance of many real-world networks is often masked by finite-size effects.

关键词： Network structure Scale free & inhomogeneous networks Small-world networks Critical exponents Renormalization Renormalization group Self-organized criticality Self-similarity

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Guest Editorial:Advances in Bio-inspired Heuristics for Computing

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CAAI Transactions on Intelligence Technology 2019年第3期4卷 127-128页

作者： XINCHAO ZHAO MAOGUO GONG XINGQUAN ZUO LINQIANG PAN School of Science Beijing University of Posts and TelecommunicationsBeijing 100876PR China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian UniversityXi’an 710071PR China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876PR China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of AutomationHuazhong University of Science and TechnologyWuhan 430074PR China

Bio-inspired computing (BIC), short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. The field of bio-inspired computing brings together researchers from many disciplines, including biology, computer science, mathematics, physics and genetics.

关键词： Guest Editorial Advances Bio-inspired Heuristics Computing

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Distributed Frequency controller for MT-HVDC Systems Via Adaptive Dynamic Programming

Distributed Frequency Controller for MT-HVDC Systems Via Ada...

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第32届中国控制与决策会议

作者： Zhongjie Hu Zhi-Wei Liu Xiong Hu Ming Chi College of Artificial Intelligence and Automation Huazhong University of Science and Technology the Key Laboratory of Image Processing and Intelligent Control Ministry of EducationHuazhong University of Science and Technology

The exact mathematical models of multi-terminal high-voltage direct current（MT-HVDC） systems are hard to be obtained in the practical application of HVDC *** overcome this challenge,a model-free distributed frequency controller for MT-HVDC systems is proposed based on Adaptive dynamic programming（ADP） in this ***,for each AC area,only the local and neighboring sampling data of frequency is required without the MT-HVDC system ***,the proposed controller is distributed which effectively balances the communication burden among the AC ***,the proposed controller makes the connected AC areas share their power reserves via HVDC grids to compensate load disturbances so that the necessary power reserves can be *** simulations carried out on a five-terminal HVDC system evaluate the performance of the proposed controller.

关键词： MT-HVDC system distributed frequency controller adaptive dynamic programming（ADP） neural network reinforcement learning

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Distributed urban freeway traffic optimization considering congestion propagation

arXiv

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arXiv 2021年

作者： Gao, Fengkun Yang, Bo Chen, Cailian Guan, Xinping Zhang, Yang Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China Shanghai Municipal Urban-Rural Construction and Transportation Development Institute China

Traffic optimization strategies are imperative for improving the performance of transportation networks. Most traffic optimization strategies only depend on traffic states of congested road segments, where congestion propagation is neglected. Therefore, we propose a distributed traffic optimization strategy for urban freeways considering the potential congested road segments caused by congestion propagation, called potential-homogeneous-area (PHA). Utilizing the historical traffic density data, we firstly quantify the effect of congestion propagation and identify PHA by applying the proposed spatio-temporal lambda-connectedness method. Meanwhile, dynamic capacity constraints of PHA are determined and are integrated with cell transmission model (CTM) in a centralized traffic optimization problem. To reduce computational complexity and improve scalability, we then propose a doubleconsensus- based alternating direction method of multipliers algorithm (DC-ADMM) to handle the neighbor coupling constraints and global coupling constraints for solving the problem in a fully distributed way. We prove that the proposed DC-ADMM algorithm converges to the optimal solution in the condition of convex objective function. Finally, simulations based on real traffic density data, collected in Inner Ring Road, Shanghai, China, reveal the effectiveness of our proposed strategy. © 2021, CC BY.

关键词： Roads and streets

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Dynamic Event-Triggered Consensus of Multi-agent Systems on Matrix-weighted Networks

arXiv

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arXiv 2021年

作者： Pan, Lulu Shao, Haibin Li, Dewei Liu, Lin Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Electronic Information Engineering Shanghai Dianji University Shanghai201306 China

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless and leader-follower consensus for multi-agent systems on matrix-weighted networks, respectively. Remarkably, the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks, generalizing those results only applicable for scalar-weighted networks. The proposed approach allows each agent to broadcast and receive information only at its triggering instants. Finally, simulation examples are provided to demonstrate the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Coordinated operation of electricity and hydrogen blended gas networks via correlation-enhanced joint chance-constrained approach

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International Journal of Hydrogen Energy 2025年 149卷

作者： Sicheng Liu Bo Yang Lun Yang Xu Yang Xin Li Zhaojian Wang Kai Ma Xinping Guan State Key Laboratory of Submarine Geoscience School of Automation and Intelligent Sensing Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory for System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an 710049 China Key Lab of Industrial Computer Control Engineering of Hebei Province and the School of Electrical Engineering Yanshan University Qinhuangdao 066000 China

Blending green hydrogen into natural gas can effectively promote hydrogen consumption and operational flexibility of electricity-gas coupled networks. However, the volatility of renewable energy sources threatens the operation of green hydrogen production and blending, while existing system-level risk management methods either exhibit over-conservatism or are computationally inefficient when constraints are numerous. Therefore, this paper proposes a correlation-enhanced distributionally robust joint chance-constrained (DRJCC) method for the joint scheduling of electricity and hydrogen-enriched compressed natural gas (E-HCNG) networks, aiming to minimize operating cost while limiting joint violation risk under renewable energy uncertainty. Specifically, before DRJCC, an improved hydrogen fraction model is developed for hydrogen blending, refining the representation of fraction variations by incorporating the buffer effect of line packs. Then, for DRJCC solving, the commonly used Bonferroni approximations are overly conservative in allocating joint violation risks to individual chance constraints, especially when constraints are numerous. To mitigate this, by leveraging strongly violation correlations of operational constraints under affine policies, a grouping-based risk allocation strategy is proposed to reduce the over-conservatism. Theoretical derivations confirm the guarantee of joint violation risk via the proposed method. Additionally, customized transformations are developed to address the non-convexities introduced by variable hydrogen fractions. Case studies demonstrate the effectiveness and superiority of the proposed method.

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