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Sequential fusion estimation for multisensor systems with non-Gaussian noises

Science China(Information Sciences) 2020年第12期63卷 153-165页

作者： Liping YAN Chenying DI Q.M.Jonathan WU Yuanqing XIA Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Department of Electrical and Computer Engineering University of Windsor

The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.

关键词： state estimation sequential fusion non-Gaussian disturbance heavy-tailed noise multivariate t-distribution

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Time-varying formation tracking control of high-order multi-agent systems with multiple leaders and multiplicative noise

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Science China(Information Sciences) 2024年第12期67卷 191-207页

作者： Ruru JIA Xiaofeng ZONG Qing WANG College of Artificial Intelligence Nankai University School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation Science and Electrical Engineering Beihang University

This work discusses the time-varying formation(TVF) tracking control problem of high-order multi-agent systems(MASs) with multiple leaders and multiplicative measurement noise. With the help of Lyapunov function tools and stochastic analysis methods, the TVF tracking protocol with multiple leaders and multiplicative noise is developed based on the relative state measurements, where followers are driven to realize the target TVF while tracking the convex combination formed by multiple leaders. Then, the TVF tracking problem is converted into the mean square asymptotic stability problem of a stochastic differential equation(SDE); sufficient conditions related to the control gains are given by stabilizing the corresponding stochastic system. Moreover, a TVF tracking algorithm is presented to outline the steps of protocol ***, the theoretical results are illustrated in terms of simulation examples.

关键词： multi-agent systems multiple leaders multiplicative measurement noise stochastic differential equation formation tracking control

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Flexible Robotic Grasping Strategy with Constrained Region in Environment

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International Journal of automation and computing 2017年第5期14卷 552-563页

作者： Chao Ma Hong Qiao Rui Li Xiao-Qing Li School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment, which is inspired by the grasping operations of the human beings. More precisely, constrained region in environment is formed by the environment, which integrates a bio-inspired co-sensing framework. By utilizing the concept of constrained region in environment, the grasping by robots can be effectively accomplished with relatively low-precision sensors. For the grasping of dexterous robotic hands, the attractive region in environment is first established by model primitives in the configuration space to generate offline grasping planning. Then, online dynamic adjustment is implemented by integrating the visual sensory and force sensory information, such that the uncertainty can be further eliminated and certain compliance can be obtained. In the end, an experimental example of BarrettHand is provided to show the effectiveness of our proposed grasping strategy based on constrained region in environment.

关键词： Grasping strategy compliant grasping dexterous robotic hands attractive region in environment constrained region inenvironment.

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Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

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Chinese Physics B 2015年第9期24卷 147-152页

作者：魏庆来宋睿卓孙秋野肖文栋 The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation and Electrical Engineering University of Science and TechnologyBeijing School of Information Science and Engineering Northeastern University

This paper estimates an off-policy integral reinforcement learning（IRL） algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman（HJB） equation, an off-policy IRL algorithm is *** is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.

关键词： adaptive dynamic programming approximate dynamic programming chaotic system optimal tracking control

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Cloud-Fog automation: The New Paradigm towards Autonomous Industrial Cyber-Physical systems

arXiv

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arXiv 2025年

作者： Jin, Jiong Pang, Zhibo Kua, Jonathan Zhu, Quanyan Johansson, Karl H. Marchenko, Nikolaj Cavalcanti, Dave School of Engineering Swinburne University of Technology HawthornVIC3122 Australia Department of Intelligent Systems KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Technology ABB Corporate Research Sweden Västerås72226 Sweden School of Information Technology Deakin University GeelongVIC3220 Australia Department of Electrical and Computer Engineering Tandon School of Engineering New York University BrooklynNY11201 United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm10044 Sweden Corporate Research Robert Bosch GmbH Renningen71272 Germany Intel Corporation Santa ClaraCA95054 United States

Autonomous Industrial Cyber-Physical systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term "3C co-design") is required. In this paper, we position Cloud-Fog automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security. © 2025, CC BY-SA.

关键词： Computer control

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LDNet-RUL: Lightweight Deformable Neural Network for Remaining Useful Life Prognostics of Lithium-ion Batteries

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IEEE Transactions on Power Electronics 2025年

作者： Wu, Xiankui Li, Penghua Deng, Zhongwei Liu, Zhitao Kurboniyon, Mekhrdod S. Xiang, Sheng Yin, Gang Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Chongqing University of Science and Technology School of Computer Science and Engineering Chongqing401331 China University of Electronic Science and Technology School of Mechanical and Electrical Engineering Chengdu611731 China Zhejiang University Institute of Cyber-Systems and Control Zhejiang 310058 China National Academy of Sciences of Tajikistan Center of Innovative Development of Science and New Technologies Dushanbe734025 Tajikistan Chongqing University College of Resource and Safety Engineering Chongqing400044 China

Deep neural networks (DNNs)-based battery remaining useful life (RUL) prognostics models suffer from high computational costs, long inference times, and a tendency to overfit, posing significant challenges for deployment on memory-constrained edge devices. To address this, LDNet-RUL, a lightweight yet high-performance model, is proposed for lithiumion battery lifetime prognostics. Specifically, a deformable depthwise convolution unit is designed that efficiently adapts to variations in input features with relatively low computational cost, which leverages fewer parameters and an adaptive receptive field to capture battery degradation dynamics. A multi-scale feature extraction method is introduced to extract degradation features from both short-term and long-term data during the battery charging process, which makes the method applicable to various battery charging protocols. To address overfitting and accelerate model inference, an adaptive channel selector, an adaptive residual connector, and a progressive dimensionalitygrouped bottleneck filter are incorporated to refine feature selection and reduce the model's complexity, boosting the model's overall performance. Experiments on vehicle lithium-ion battery datasets demonstrate that LDNet-RUL achieves state-of-the-art (SOTA) performance, with average RMSE of 0.23 and Score of 0.49. The model is lightweight, with only 7.92K parameters, achieves inference times of 3.94ms on GPU and CPU, and 5.39ms on the NVIDIA Jetson TX2. © 1986-2012 IEEE.

关键词： Convolution

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A zeroth-order algorithm for distributed optimization with stochastic stripe observations

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Science China(Information Sciences) 2023年第9期66卷 297-298页

作者： Yinghui WANG Xianlin ZENG Wenxiao ZHAO Yiguang HONG School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Department of Control Science and Engineering Tongji University

Recent years have seen a rising interest in distributed optimization problems because of their widespread applications in power grids, multi-robot control, and regression *** the last few decades, many distributed algorithms have been developed for tackling distributed optimization problems. In these algorithms, agents over the network only have access to their own local functions and exchange information with their neighbors.

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Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series

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Journal of Central South University 2017年第3期24卷 692-698页

作者： ZENG Ming LI Jing-hai MENG Qing-hao ZHANG Xiao-nei Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control （School of Electrical Automation and Information Engineering Tianjin University） Tianjin 300072 China

Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern ***,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than *** indicates that all these wind speed time series have non-stationary ***,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer ***,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.

关键词： temporal-spatial cross-correlation near-surface wind speed time series detrended cross-correlation analysis （DCCA） cross-correlation coefficient Pearson coefficient cross-correlation function

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Verification of monocular and binocular pose estimation algorithms in vision-based UAVs autonomous aerial refueling system

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Science China(Technological Sciences) 2016年第11期59卷 1730-1738页

作者： LI Han DUAN HaiBin Bio-inspired Autonomous Flight Systems (BAFS) Research Group Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University

With the high focus on autonomous aerial refueling(AAR), it becomes increasingly urgent to design efficient methods or algorithms for solving the AAR problems in complicated aerial environments. A vision-based technology for AAR is developed in this paper, and five monocular and binocular visual algorithms for pose estimation of the unmanned aerial vehicles(UAVs) are adopted and verified in this AAR system. The real-time on-board vision system is also designed for precise navigation in the UAVs docking phase. A series of out-door comparative experiments for different pose estimation algorithms are conducted to verify the feasibility and accuracy of the vision algorithms in AAR.

关键词： unmanned aerial vehicles（UAVs） autonomous aerial refueling（AAR） pose estimation monocular vision binocular vision

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Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design

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Science China(Technological Sciences) 2015年第11期58卷 1915-1923页

作者： QIU HuaXin DUAN HaiBin Bio-inspired Autonomous Flight Systems(BAFS) Research Group Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University

Pigeon-inspired optimization(PIO) is a new swarm intelligence optimization algorithm, which is inspired by the behavior of homing pigeons. A variant of pigeon-inspired optimization named multi-objective pigeon-inspired optimization(MPIO) is proposed in this paper. It is also adopted to solve the multi-objective optimization problems in designing the parameters of brushless direct current motors, which has two objective variables, five design variables, and five constraint variables. Furthermore, comparative experimental results with the modified non-dominated sorting genetic algorithm are given to show the feasibility, validity and superiority of our proposed MIPO algorithm.

关键词： brushless direct current （BLDC） motor multi-objective pigeon-inspired optimization （MPIO） electromagnetics mul-ti-objective optimization

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