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An emulation approach to semi-global robust output regulation for a class of nonlinear uncertain systems

IEEE/CAA Journal of Automatica Sinica 2025年

作者： Wu, Jieshuai Lu, Maobin Deng, Fang Chen, Jie School of Control Science and Engineering Tiangong University Tianjin300387 China School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Beijing Institute of Technology Beijing10081 China Tongji University Shanghai200092 China

In this paper, we investigate the semi-global robust output regulation problem of a class of nonlinear networked control systems. By the emulation approach, we propose a class of sampled-data output feedback control laws to solve this problem. In particular, we first develop a general sampled-data dynamic output feedback control law and characterize the closed-loop system by a hybrid system. Then, we design the internal model based on the sampled error output of the system. Based on the internal model principle, we convert the semi-global robust output regulation problem into a semi-global robust stabilization problem of an augmented hybrid system composed of the internal model and the original system. By proposing the sampled error output feedback control law and by means of Lyapunov analysis, we obtain the maximum allowable transmission interval for sampling and show that semi-global robust stabilization of the augmented hybrid system can be achieved by the proposed sampled-data control law and thus leading to the solution of the semi-global robust output regulation problem. Finally, we apply the proposed control approach to two practical applications to verify the effectiveness of the proposed control approach. © 2014 Chinese Association of automation.

关键词： Networked control systems

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Accelerated Optimized Topology Design in Affine Formation control Using ADMM

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IEEE Transactions on Network Science and engineering 2025年

作者： Wang, Yumeng Yang, Qingkai Xiao, Fan Fang, Hao Chen, Jie School of Automation Beijing100081 China Beijing Institute of Technology Greater Bay Area Innovation Research Institute Zhuhai519088 China China Aerospace Science and Technology Institute of Remote Sensing Satellite Beijing100094 China Tongji University Department of Control Science and Engineering Shanghai201804 China

This paper studies the problem of topology design for activating affine formation control schemes. The affine formation control exhibits its unique feature as it relies on the stress matrix to dynamically maneuver the whole formation by controlling a small number of agents. Network properties of interest for this design problem generally give rise to optimization formulations within the framework of mixed- integer semidefinite programming (MISDP), resulting in computational inefficiency and NP-hardness. Firstly, to avoid introducing binary variables, the optimization of communication cost is modeled as an l_{1}-regularized network sparsity problem. In this way, an optimized topology design method accelerated by the alternating direction method of multipliers (ADMM) is proposed to obtain the stress matrix with low communication cost, fast convergence speed and high tolerance to time-delay. Furthermore, addressing scenarios irrespective of whether the minimum eigenvalue of the stress matrix is prescribed, we propose two enhanced ADMM-based algorithms with closed-form solutions. This is achieved through the transformation of semi-definite constraints in the subproblem into equality constraints. Finally, comparative simulations demonstrate the accelerated effects of the proposed scheme, showcasing its effectiveness in interaction topology construction and optimization for large-scale networks. © 2025 IEEE. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Decoupling Gyro Bias from Attitude Estimation in PAHRS: A Time-Differenced Modeling Approach for Polarization Sensor Measurement

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Liu, Xin Yang, Jian Qiao, Jianzhong Li, Wenshuo Guo, Lei Wang, Wei Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Hangzhou Innovation Institute Hangzhou310051 China China Aerospace Science and Technology Corporation Beijing100048 China Beijing Institute of Aerospace Control Devices Beijing100039 China

The polarization-based attitude and heading reference system (PAHRS) is able to provide attitude and heading information in the case of global navigation satellite system (GNSS) signal degradation. A key issue in PAHRS data processing is the estimation of the gyro bias and attitude misalignment angle, which could have a significant impact on the overall attitude determination performance. In the existing polarization sensor (PS)/inertial navigation system (INS) fusion models, however, the gyro bias and attitude misalignment angle are coupled. As a result, the error of gyro bias estimation and that of attitude misalignment angle estimation cannot be separated. When the attitude misalignment angle has the estimation errors (e.g. acceleration scenario), the coupling effect between gyro bias and attitude misalignment angle will further aggravate the estimation errors, which could seriously degrade the attitude determination accuracy. To improve the accuracy and adaptability of PAHRS, in this article, we propose a time-differenced PS (TDPS)/INS modeling approach and present a polarization units selection strategy for information extraction from PS and INS. Different from the existing PS/INS fusion models, only gyro bias appears in the TDPS measurement model, thus eliminating the impact of attitude misalignment angle errors on gyro bias estimation. With the estimated gyro bias, a compensation term is constructed for attitude misalignment angle estimation in the TDPS/INS data fusion process. In this way, decoupling of the gyro bias from attitude misalignment angle estimation can be achieved. Moreover, a polarization units selection strategy is proposed, the reliable polarization units in PS can be thereby selected adaptively to fuse with INS, which can reduce the effect of unreliable polarization data. Finally, effectiveness and practicality of the proposed TDPS/INS modeling and data fusion approaches are demonstrated via both numerical simulations and vehicle test. © 1965

关键词： Inertial navigation systems

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Thick 3D-Printed Hierarchical Li4Ti5O12@MOF anode and Grid-Lined micropores for High-Performance Lithium-Ion batteries

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Journal of Colloid and Interface Science 2025年 696卷 137883页

作者： Mwizerwa, Jean Pierre Li, Jie Nsengiyumva, Walter Li, Chen Xu, Kun Rasaki, Sefiu Abolaji Liu, Changyong School of Materials Science and Engineering Fujian University of Technology Fuzhou350118 China Additive Manufacturing Institute College of Mechatronics & Control Engineering Shenzhen University Shenzhen518060 China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and Automation Fuzhou University Fuzhou China The Institute of Precision Instrument and Intelligent Measurement & Control Fuzhou University Fuzhou China Institute: Department of Chemical and Petroleum Engineering University of Calgary AlbertaT2N 1N4 Canada

Li4Ti5O12 (LTO) anode is a promising candidate for high-energy–density lithium-ion batteries (LIBs), but achieving high mass loading, a porous structure, and efficient ion transport remains a challenge. Herein, we present a high-mass-loading, hierarchically porous LTO anode with a conductive network and grid-lined micropores, embedded with a metal–organic framework (MOF) as both an electrode additive and surface protective layer. This novel structure is fabricated using extrusion-based three-dimensional (3D) printing technology. The self-standing framework provides mechanical stability and high conductivity, enabling a thick (316 μm) 3D-printed LTO@UiO-66-MOF (3D-LTO@U) anode with a mass loading of 11 mg/cm2. It delivers a high-rate capability of 161 mAh/g at 5C, an areal specific capacity of 5.37 mAh/cm2, and 78.9 % areal capacity retention after 150 cycles. Additionally, it achieves a high specific energy density of 382.35 Wh/kg. The UiO-66 MOF provides strong binding affinity, suppressing side reactions and enhancing Li-ion/electron transport within the 3D-printed interconnected channels. This improves active material utilization during charge and discharge. Furthermore, a 3D-printed full cell integrating a grid-lined 3D-LTO@U anode and a 3D-printed LiFePO4 cathode exhibits enhanced electrochemical performance. This work demonstrates an effective strategy for designing thick, high-mass-loading, and porous conductive network anodes for advanced LIBs. © 2025 Elsevier Inc.

关键词： Lithium-ion batteries

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Torque Characteristic Analyses and Multi-Objective Optimisation of Multi-Layer Flux-Barrier Less-Rare-Earth Permanent Magnet Synchronous Machine

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IET Electric Power Applications 2025年第1期19卷

作者： Wang, Weinan Gong, Weize Zhu, Liangkuan Wei, Jian Wang, Mingqiao Liu, Yong Zheng, Ping College of Computer and Control Engineering Northeast Forestry University Harbin China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

This paper investigates the torque characteristic and multi-objective optimisation (MOO) of the multi-layer flux-barrier less-rare-earth permanent magnet synchronous machine (MLFB-LRE-PMSM) used for the electric vehicles (EVs). This study explores the variation of torque characteristics with current angle under different winding current conditions, and thoroughly analyses the influence of permanent magnet (PM) structure parameters on reluctance torque, PM torque and the proportion of reluctance torque in the electromagnetic torque. On this basis, the sensitivity analysis method based on Sobol sequence and joint Sobol index is adopted, which not only simplifies the traditional analysis process, but also effectively considers the interaction effect between the optimisation objectives. Finally, a cooperative optimisation strategy of improved whale optimisation algorithm (WOA) and genetic algorithm (GA) is proposed, which is successfully applied to the MOO design of MLFB-LRE-PMSM. The results show that the improved WOA algorithm shows excellent optimisation performance and can be used as a new solution in the field of motor optimisation. At the same time, the engineering practicability of the proposed collaborative optimisation scheme is verified by finite element simulation. © 2025 The Author(s). IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Synchronous machinery

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Barrier Offset Varying-Parameter Dynamic Learning Network for Solving Dual-Arms Human-Like Behavior Generation

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Zhang, Zhijun Zhang, Mingyang Guo, Jinjia He, Haotian School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Key Library of Autonomous Systems and Network Control Ministry of Education China Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China Guangzhou510335 China Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shanxi University of Technology Hanzhong723001 China School of Information Science and Engineering Changsha Normal University Changsha410100 China School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China School of Computer Science and Engineering Jishou University Jishou416000 China

Enabling robots to imitate human actions and perform tasks with high precision while avoiding potential obstacles in the environment can effectively enhance the interaction between social robots and humans. In this paper, to achieve higher precision trajectory tracking and obstacle avoidance for dual-arm humanoid robots, the barrier offset varying-parameter dynamic learning neural (BOVDL) network method is proposed and applied to dual-arm humanoid behavior generation scheme. To do so, a dual-arms humanoid robot model is set up, and transformed into a constrained time-varying quadratic programming (TVQP) problem. Secondly, by using lagrangian multiplier method and Karush-Kuhn-Tuchker condition, the inequality constrained TVQP is converted as a time-varying equation with a barrier parameter. Thirdly, a varying-parameter dynamic learning network is presented to solve the time-varying equation with a barrier parameter. Computer simulation experiments are conducted to verify the feasibility, accuracy, and safety of the proposed BOVDL network method. Experimental results show that all 14 joints of the humanoid robot's arms are within the motion range of each real human arm's physical constraints. The maximum position error and velocity error between the desired trajectory and the actual trajectory of the end effector are less than 10-6m magnitude and 10-7m magnitude, respectively, representing a reduction of 5 orders of magnitude compared to the traditional varying-parameter convergent-differential neural network. Furthermore, the proposed method also enables the dual-arm humanoid robot to avoid collisions with obstacles while performing tasks, demonstrating the superiority of the proposed BOVDL network scheme. © 2016 IEEE.

关键词： Anthropomorphic robots

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A Transfer Gated Recurrent Unit Model for Battery Health Monitoring Under Cross-Domain Conditions

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Tian, Jilun Xie, Jiale Zhang, Jiusi Huang, Congsheng Jiang, Yuchen Luo, Hao Chow, Mo-Yuen Yin, Shen Harbin Institute of Technology School of Astronautics Department of Control Science and Engineering Harbin150001 China North China Electric Power University Department of Automation Baoding071003 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Norwegian University of Science and Technology Faculty of Engineering Department of Mechanical and Industrial Engineering Trondheim7034 Norway

Battery health monitoring is crucial for ensuring maintenance reliability and safety, with broad applications in various industrial systems. Data-driven state-of-health (SOH) estimation methods can perform excellently using massive historical data without prior physical knowledge. In the battery aging process, this article extracts the essential health indicators to construct the model and adopts a suitable data preprocessing method to reduce the influence of noise. There is still a limitation for data-driven methods regarding data quality, and the domain shift caused by different operating conditions will damage estimation performance. To tackle the issue, this article proposes a transfer feature transformation (TFT) based on mean maximum discrepancy (MMD) to get a new feature space to get domain-invariant features with sufficient information. We propose a multilayer gated recurrent unit (GRU) neural network with fully connection (FC) layers to uncover hidden relationships in degraded data. Fusing TFT and GRU-FCs methods can realize an end-to-end SOH estimation under cross-domain conditions. We apply real-world public Center of Advanced Life Cycle engineering (CALCE) and NASA battery datasets and lab testing-based accelerated aging data to validate the performance of algorithms. By comparison, our proposed TFT-GRU-FCs method can get the best performance of all algorithms, and we have also given sufficient results as support. © 1963-2012 IEEE.

关键词： Electronic health record

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GROVE: A Generalized Reward for Learning Open-Vocabulary Physical Skill

arXiv

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arXiv 2025年

作者： Cui, Jieming Liu, Tengyu Meng, Ziyu Yu, Jiale Song, Ran Zhang, Wei Zhu, Yixin Huang, Siyuan Institute for Artificial Intelligence Peking University China State Key Laboratory of General Artificial Intelligence BIGAI China School of Control Science and Engineering Shandong University China Department of Automation Tsinghua University China

Learning open-vocabulary physical skills for simulated agents presents a significant challenge in Artificial Intelligence (AI). Current Reinforcement Learning (RL) approaches face critical limitations: manually designed rewards lack scalability across diverse tasks, while demonstration-based methods struggle to generalize beyond their training distribution. We introduce GROVE, a generalized reward framework that enables open-vocabulary physical skill learning without manual engineering or task-specific demonstrations. Our key insight is that Large Language Models (LLMs) and Vision Language Models (VLMs) provide complementary guidance—LLMs generate precise physical constraints capturing task requirements, while VLMs evaluate motion semantics and naturalness. Through an iterative design process, VLM-based feedback continuously refines LLM-generated constraints, creating a self-improving reward system. To bridge the domain gap between simulation and natural images, we develop Pose2CLIP, a lightweight mapper that efficiently projects agent poses directly into semantic feature space without computationally expensive rendering. Extensive experiments across diverse embodiments and learning paradigms demonstrate GROVE’s effectiveness, achieving 22.2% higher motion naturalness and 25.7% better task completion scores while training 8.4× faster than previous methods. These results establish a new foundation for scalable physical skill acquisition in simulated environments. © 2025, CC BY-NC-SA.

关键词： Reinforcement learning

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Flexible prescribed performance-based approaching control for UAV aerial recovery under multiwind disturbances

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Chinese Journal of Aeronautics 2025年第4期 424-440页

作者： Yanxiang WANGa Honglun WANG Junfan ZHU Yiheng LIU Zikang SUe School of Automation Science and Electrical Engineering Beihang University The Science and Technology on Aircraft Control Laboratory Beihang University Shenyuan Honors College Beihang University Beijing Institute of Astronautical Systems Engineering College of Automation Engineering Nanjing University of Aeronautics and Astronautics

This article investigates the approaching control for fixed-wing Unmanned Aerial Vehi-cle(UAV) aerial recovery in the presence of pre-specified performance requirements, complex air-flows, maneuvering flight of transport aircraft, and different initial deviations. First, a novelcontrol-oriented Six-Degree-Of-Freedom(6-DOF) UAV model considering airflow disturbancesis established for better consistency with the actual UAV system. Then, to achieve satisfactory per-formance in the approaching process, a Flexible Appointed-time Prescribed Performance control(FAPPC) algorithm, with the features of user-specified time convergence, no overshoot, indepen-dence from the initial value, and singularity-free, is proposed. Specifically, to solve the singularityissue encountered by the existing PPC methods in dealing with sudden disturbances, an adaptiveadjustment signal is introduced in FAPPC to perceive the threat of increasing error and relax thepreset boundaries appropriately. Moreover, minimum learning parameter-based neural networkestimators are developed to approximate unknown lumped disturbances at a low computationalcost. Finally, the stability of the closed system is analyzed via Lyapunov synthesis, and the effective-ness and advantages of the proposed control scheme are demonstrated via simulation andHardware-In-the-Loop(HIL) experimental validation.

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Analysis of multi-field cavitation structure and dynamic mode decomposition based on a separated venturi cavitation generator

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European Journal of Mechanics, B/Fluids 2025年 113卷

作者： Wang, Xiaoyu Wang, Tian Ma, Junqi Sun, Qiang Lv, WanYu School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Shenyang110819 China AECC Shenyang Engine Research Institute Shenyang110015 China

Cavitation is generated due to the reduction of fluid pressure, which will cause instability and erosion inside the pipeline, valve or pump etc. In this paper, based on the improved venturi cavitation generator, the cavitation under several different flow conditions is studied, and a high-speed camera is used to capture the dynamic process of cavitation at the venturi throat. The flow field simulation data have been analysed using the dynamic mode decomposition method, and the coherent structures of sheet cavitation, transition zone cavitation, and cloud cavitation and their displacements with cycle time have been investigated. The periodic variation of cavitation is analysed using a combination of experimental and numerical simulations, and the state of cavitation in the flow field is investigated in detail using data-driven tools. The results show that periodic changes in the cavitation structure can be observed intuitively through the improved venturi generator and agrees well with the results of simulation. Cavitation intensity is continuously enhanced with the increase of inlet velocity. At flow velocity of 4.68 m/s, the cavitation is manifested as a piecewise cavitation that can only be observed at the throat position, since then the length of the cavitation cloud continues to increase, finally the detachment of a bubble cloud and localized vortex cavitation are observed at velocity of 6.08 m/s. The frequency and growth characteristics of individual flow modes can be accurately captured using the dynamic modal decomposition of the flow field. © 2025

关键词： High speed cameras

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