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Journal of Bionic Engineering 2024年第1期21卷 149-165页

作者： Xiangang Huang Chenghao zhang Wenqi Feng Xiangye zhang Deyuan zhang Yanqiang Liu Institute of Bionic and Micro-Nano Systems School of Mechanical Engineering and AutomationBeihang UniversityBeijing 100191China

A variety of soft wall-climbing robots have been developed that can move in certain *** of these soft robots can only move on conventional surfaces and lack adaptability to complex *** the adaptability of soft robots on complex surfaces is still a challenging *** this end,we study the layered structure of the starfish tube foot and the valve flap structure in the water vascular system,and use an ultrasonic stress detector to study the stiffness distribution of the arm *** by the motion of the starfish,we present a bionic soft wall-climbing robot,which is driven by two groups of pneumatic feet and achieves body bending through active adaptation *** design the structure of the foot to flex to provide driving force,and there are suction cups at the end of the foot to provide *** soft foot has a simple structure design,adapts to a variety of surfaces,and does not damage the surface of the *** stiffness layers achieve stiffness changes by the principle of line *** Central Pattern Generator theory is introduced to coordinately control the multiple feet of the *** experiments,we verify the adaptability of the soft robot to curved *** research may provide a reference for the design and development of crawling soft robots on complex surfaces.

关键词： Starfish Tube foot Biomimetic robot Soft robot Climbing robot

Distributed Multicircular Circumnavigation Control for UAVs with Desired Angular Spacing

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Defence Technology（防务技术） 2024年第1期31卷 429-446页

作者： Shixiong Li Xingling Shao Wendong zhang Qingzhen zhang National Key Laboratory for Electronic Measurement Technology School of Instrument and ElectronicsNorth University of ChinaTaiyuan 030051China school of automation Science and Electrical Engineering Beihang UniversityBeijing 100086China

This paper addresses a multicircular circumnavigation control for UAVs with desired angular spacing around a nonstationary *** defining a coordinated error relative to neighboring angular spacing,under the premise that target information is perfectly accessible by all nodes,a centralized circular enclosing control strategy is derived for multiple UAVs connected by an undirected graph to allow for formation behaviors concerning the moving ***,to avoid the requirement of target’s states being accessible for each UAV,fixed-time distributed observers are introduced to acquire the state estimates in a fixed-time sense,and the upper boundary of settling time can be determined offline irrespective of initial properties,greatly releasing the burdensome communication ***,with the aid of fixed-time distributed observers,a distributed circular circumnavigation controller is derived to force all UAVs to collaboratively evolve along the preset circles while keeping a desired angular *** is inferred from Lyapunov stability that all errors are demonstrated to be *** are offered to verify the utility of proposed protocol.

关键词： Angular spacing Distributed observer Multicircular circumnavigation Moving target UAVs

Deep reinforcement learning based active surge control for aeroengine compressors

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Chinese Journal of Aeronautics 2024年第7期37卷 418-438页

作者： Xinglong zhang Zhonglin LIN Runmin JI Tianhong zhang College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China school of Mechanical Engineering and automation Fuzhou UniversityFuzhou 350108China

This study proposes an active surge control method based on deep reinforcement learning to ensure the stability of compressors when adhering to the pressure rise command across the wide operating range of an ***,the study establishes the compressor dynamic model with uncertainties,disturbances,and Close-Coupled Valve(CCV)actuator *** upon this foundation,a Partially Observable Markov Decision Process(POMDP)is defined to facilitate active surge *** address the issue of unobservability,a nonlinear state observer is designed using a finite-time high-order sliding ***,an Improved Soft Actor-Critic(ISAC)algorithm is developed,incorporating prioritized experience replay and adaptive temperature parameter techniques,to strike a balance between exploration and convergence during *** addition,reasonable observation variables,error-segmented reward functions,and random initialization of model parameters are employed to enhance the robustness and generalization ***,to assess the effectiveness of the proposed method,numerical simulations are conducted,and it is compared with the fuzzy adaptive backstepping method and Second-Order Sliding Mode Control(SOSMC)*** simulation results demonstrate that the deep reinforcement learning based controller outperforms other methods in both tracking accuracy and ***,the proposed active surge controller can effectively ensure stable operation of compressors in the high-pressure-ratio and high-efficiency region.

关键词： Aeroengine surge Active surge control Moore-Greitzer model Deep reinforcement learning Soft actor-critic Nonlinear observer

Multi-circular formation control with reinforced transient profiles for nonholonomic vehicles:A path-following framework

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Defence Technology（防务技术） 2024年第1期31卷 278-287页

作者： Jintao zhang Xingling Shao Wendong zhang Zongyu Zuo school of Instrument and Electronics North University of ChinaTaiyuan 030051China State Key Laboratory of Dynamic Measurement Technology North University of ChinaTaiyuan 030051China Seventh Research Division School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100191China

This article investigates a multi-circular path-following formation control with reinforced transient profiles for nonholonomic vehicles connected by a digraph.A multi-circular formation controller endowed with the feature of spatial-temporal decoupling is devised for a group of vehicles guided by a virtual leader evolving along an implicit path,which allows for a circumnavigation on multiple circles with an anticipant angular *** addition,notice that it typically imposes a stringent time constraint on time-sensitive enclosing scenarios,hence an improved prescribed performance control(IPPC)using novel tighter behavior boundaries is presented to enhance transient capabilities with an ensured appointed-time convergence free from any *** significant merits are that coordinated circumnavigation along different circles can be realized via executing geometric and dynamic assignments independently with modified transient ***,all variables existing in the entire system are analyzed to be *** and experimental results are provided to validate the utility of suggested solution.

关键词： Multi-circular formation Reinforced transient profiles Nonholonomic vehicles Path following

Accumulated-state-error-based event-triggered sampling scheme and its application to H∞ control of sampled-data systems

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Science China(Information Sciences) 2024年第6期67卷 328-341页

作者： Xian-Ming zhang Qing-Long HAN Bao-Lin zhang Xiaohua GE Dawei zhang school of Science Computingand Engineering TechnologiesSwinburne University of Technology College of automation and Electronic Engineering Qingdao University of Science & Technology school of Mathematics Shandong University

This paper is concerned with event-triggered H∞control of sampled-data systems. Its novelties lie in three aspects:（i） A novel accumulated-state-error-based event-triggered scheme is introduced by comparing the integral of the state error from tk to t with the system state sampled at tk. This condition works well due to the fact that the so-called Zeno behaviour does not occur.（ii） A novel Lyapunov functional is constructed to establish a criterion to ensure some certain H∞performance of the closed-loop system. This Lyapunov functional is dependent on the integral of the state error involved in the event-triggered scheme.（iii） Under the event-triggered sampling scheme, suitable state-feedback controllers can be designed rather than be given a priori. Moreover, a self-triggered implementation of the proposed event-triggered sampling scheme is presented as well. Finally, a batch reactor model and an inverted pendulum system are given to demonstrate the effectiveness of the proposed method.

关键词： event-triggered sampling self-triggered implementation $H_\infty$ control looped-functional

Parallel System Based Quantitative Assessment and Self-evolution for Artificial Intelligence of Active Power Corrective Control

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 13-28页

作者： Tianyun zhang Jun zhang Feiyue Wang Peidong Xu Tianlu Gao Haoran zhang Ruiqi Si school of Electrical Engineering and automation Wuhan UniversityWuhan 430072HubeiChina State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In artificial intelligence(AI)based-complex power system management and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,nearly no standard technical framework for objective and quantitative intelligence *** this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation *** this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment *** results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.

关键词： AI quantitative intelligence assessment and self-evolution automated reinforcement learning Bayesian optimization corrective control parallel system

Adaptive Numerical Approach for Optimal Control of a Single Train

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Journal of Systems Science & Complexity 2019年第4期32卷 1053-1071页

作者： WU Xiang zhang kanjian CHENG Ming school of Mathematical Sciences Guizhou Normal UniversityGuiyang 550001China school of Electrical Engineering Southeast UniversityNanjing 210096China school of automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of CSE Ministry of EducationSoutheast UniversityNanjing 210096China

This paper considers the optimal control problem of a single train,which is formulated as an optimal control problem of nonlinear systems with switching *** switching sequence and the switching time are decision variables to be chosen *** speaking,it is very difficult to solve this problem analytically due to its nonlinear nature,the complexity of the controller,and the existence of system state and control input *** obtain the numerical solution,by introducing binary functions for every value of the control input,relaxing the binary functions,and imposing a penalty function on the relaxation,the problem is transformed into a parameter optimization problem,which can be efficiently solved by using any gradient-based numerical ***,the authors propose an adaptive numerical approach to solve this *** results indicate that any optimal solution of the parameter optimization problem is also an optimal solution of the original ***,an optimal control problem of a single train illustrates that the adaptive numerical approach proposed by us is less time-consuming and obtains a better cost function value than the existing approaches.

关键词： Adaptive numerical approach nonlinear system optimal control switching controller train control

Modeling of LF refining process:a review

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Journal of Iron and Steel Research International 2024年第2期31卷 289-317页

作者： Zi-cheng Xin Jiang-shan zhang Kai-xiang Peng Jun-guo zhang Chun-hui zhang Qing Liu State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing100083China school of automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Tangshan Branch Hebei Iron and Steel Co.Ltd.Tangshan063000HebeiChina Xinjiang Bayi Iron and Steel Co. Ltd.Baosteel GroupWulumuqi830022XinjiangChina

With the increasing demand for energy conservation and emission reduction,more attentions have been paid to the intelligentization,greenization and low carbonization during the transformation and upgrading of steelmaking *** furnace(LF)refining is one of the key procedures in steelmaking process and has been widely used in steelmaking plants for its high equipment matching degree,low equipment investment and outstanding refining *** to the main tasks of LF refining process,the modeling methods of temperature prediction model,slag-making model,alloying model,argon blowing model and model of inclusions behavior were systematically reviewed,and the advantages and disadvantages of each modeling method were *** addition,the technical framework for the future has also been proposed based on existing works,including classification of raw materials,graphic representation of knowledge,introduction,upgradation and management of device/equipment,customization of steelmaking,modeling of refining process,synergy of models,intelligentization of decision-making,automation of control,and digitization of processes and operations,aiming to provide a reference for the modeling and intelligent development of LF refining process.

关键词： LF refining Temperature prediction Slag-making Alloying Argon blowing Inclusion behavior Modeling method

A comprehensive analysis of DAC-SDC FPGA low power object detection challenge

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Science China(Information Sciences) 2024年第8期67卷 300-320页

作者： Jingwei zhang Guoqing LI Meng zhang Xinye CAO Yu zhang Xiang LI Ziyang CHEN Jun YANG school of Electronics Science and Engineering Southeast University

The lower power object detection challenge(LPODC) at the IEEE/ACM Design automation Conference is a premier contest in low-power object detection and algorithm(software)-hardware co-design for edge artificial intelligence, which has been a success in the past five years. LPODC focused on designing and implementing novel algorithms on the edge platform for object detection in images taken from unmanned aerial vehicles(UAVs), which attracted hundreds of teams from dozens of countries to participate. Our team SEUer has been participating in this competition for three consecutive years from 2020 to 2022 and obtained sixth place respectively in 2020 and 2021. Recently, we achieved the championship in 2022. In this paper, we presented the LPODC for UAV object detection from 2018 to 2022, including the dataset, hardware platform,and evaluation method. In addition, we also introduced and discussed the details of methods proposed by each year's top three teams from 2018 to 2022 in terms of network, accuracy, quantization method, hardware performance, and total score. Additionally, we conducted an in-depth analysis of the selected entries and results, along with summarizing representative methodologies. This analysis serves as a valuable practical resource for researchers and engineers in deploying the UAV application on edge platforms and enhancing its feasibility and reliability. According to the analysis and discussion, it becomes evident that the adoption of a hardware-algorithm co-design approach is paramount in the context of tiny machine learning(TinyML).This approach surpasses the mere optimization of software and hardware as separate entities, proving to be essential for achieving optimal performance and efficiency in TinyML applications.

关键词： algorithm-hardware co-design convolutional neural networks field programmable gate array low power object detection tiny machine learning

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