Article
Mathematics
You Li, Haizhao Liang
Summary: In this paper, robust finite-time control algorithms for satellite attitude maneuvers are proposed by modifying the standard sliding mode. The fixed sliding mode is also modified to achieve finite-time stability. The proposed sliding modes greatly improve the system performance near the equilibrium point and demonstrate robustness to typical perturbations through simulation results.
Article
Engineering, Aerospace
Lorenzo Porcelli, Alejandro Pastor, Alejandro Cano, Guillermo Escribano, Manuel Sanjurjo-Rivo, Diego Escobar, Pierluigi Di Lizia
Summary: This paper introduces a novel approach to tackle the problem of maneuver detection and estimation of Resident Space Objects (RSOs) in the space environment. The proposed methodology aims to increase the flexibility of real-time cataloging systems and has been validated through testing in a simulated maintenance chain.
Article
Engineering, Aerospace
Zhongjie Meng, Hongli Liang
Summary: This paper proposes an adaptive attitude maneuver control scheme to improve the attitude precision of satellites with large-scale antennas. A novel robust input shaper is designed to suppress vibration, and an adaptive terminal sliding mode controller with radial basis function neural network is utilized for attitude tracking. The effectiveness of the proposed control scheme is validated under different space thermal environments.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Li You, Ye Dong, Xiao Bing
Summary: This paper proposes a finite time controller with a PD-like structure for satellite attitude control. The controller has a simple structure based on a standard PD controller and is designed to have both strong robustness and finite time convergence rate. The advantages of finite time control and PD control are combined in this controller. The system stability and performance are demonstrated through numerical simulation results.
DEFENCE TECHNOLOGY
(2023)
Article
Multidisciplinary Sciences
Saleem Riaz, Rong Qi, Onder Tutsoy, Jamshed Iqbal
Summary: This paper proposes a learning-based adaptive control approach to improve the position tracking of the Permanent Magnet Synchronous Motor (PMSM) servo system in the presence of friction uncertainty. A servo model involving the Stribeck friction dynamics is formulated, and unknown friction parameters are identified by a genetic algorithm. An Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) is designed to mitigate the effects of friction. Simulation results show that the proposed APD-ILC significantly improves the control performance of the PMSM in low speeds.
Article
Engineering, Aerospace
Yijun Lian, Junhua Xiang, Yong Zhao
Summary: This work presents a control method for attitude tracking of satellites using on-off electric thrusters of constant magnitude. It utilizes the Pulse-Width-Pulse-Frequency (PWPF) algorithm to achieve globally stable finite-time control, with two sliding surfaces designed for finite-time convergence. The proposed method also includes an automatic generator for generating time-related key parameters.
ADVANCES IN SPACE RESEARCH
(2023)
Article
Engineering, Aerospace
Hongyang Dong, Xiaowei Zhao, Qinglei Hu, Haoyang Yang, Pengyuan Qi
Summary: This article addresses optimal attitude tracking control tasks for rigid bodies using a reinforcement-learning-based control scheme. It proposes a constrained parameter estimator to accurately compensate for system uncertainties, ensuring exponential convergence of estimation errors and maintaining estimates within predetermined bounds. A critic-only adaptive dynamic programming (ADP) control strategy is proposed to learn the optimal control policy, without requiring the matching condition on reference control signals commonly used in relevant ADP designs. The study proves the uniform ultimate boundedness of tracking errors and critic weight's estimation errors under finite excitation conditions and verifies the effectiveness of the proposed method through numerical simulations and hardware-in-the-loop experimental tests.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Automation & Control Systems
Bogdan D. Ciubotaru, Andrei Sperila, Sabin Diaconescu, Florin Stoican, Adrian M. Stoica, Samir Bennani
Summary: In this paper, a composite and multi-technique-based controller is proposed for a three-stage micro-launcher, which achieves robust stability and performance through a combination of linear and nonlinear components and compensates for uncertainties.
CONTROL ENGINEERING PRACTICE
(2022)
Article
Automation & Control Systems
Soheila Babaei Faramarz, Ali Akbarzadeh Kalat
Summary: This paper presents a new robust back-stepping control method for attitude control of a rigid satellite. The method takes into account the total uncertainty of inertia and external disturbance while only measuring the outputs of the system. It also estimates the upper bound of the norm of the total uncertainty vector adaptively and uses a finite-time extended observer to estimate the angular velocity and total disturbance. The robust stability of the control system is proved using Lyapunov theory, and a numerical simulation demonstrates the effectiveness of the proposed control method.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2023)
Article
Engineering, Aerospace
Jirong Qi, He Liao, Yufei Xu, Zhu Zhu, Chaolan You
Summary: The study proposed an attitude tracking controller using event-triggering mechanism to reduce communication burden and actuator asynchrony between non-contact close-proximity formation satellites, thus improving system stability and performance. The feasibility and effectiveness of this approach was demonstrated through numerical simulations.
Article
Engineering, Aerospace
Meysam Jokar, Hassan Salarieh, Hossein Nejat Pishkenari
Summary: This study addresses the attitude control of a liquid propellant-filled satellite considering the effects of fuel sloshing. A PDE-based controller is proposed to achieve satellite attitude control and suppress fuel sloshing. The derived coupled equations using Hamilton's principle are used to analyze the stability of the closed-loop system. The evaluation of the proposed controller demonstrates its significance in achieving more accurate satellite attitude maneuvers.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Xiuqin Wang, Rui Zhang, Guoli Li, Qunjing Wang, Yan Wen
Summary: This paper proposes a time delay estimation controller based on gradient compensation to improve the trajectory tracking performance of a multi-degree-of-freedom Permanent Magnet Spherical Actuator (PMSpA). The dynamic model of the PMSpA is derived to estimate and simplify the nonlinear terms using the time delay estimation method. A gradient compensator is introduced to correct and compensate for the estimation errors caused by time delay control. The stability of the designed controller is proved using the Lyapunov equation. Simulation and experimental results demonstrate the correctness and effectiveness of the controller in improving the control accuracy of the spherical motor.
Article
Engineering, Multidisciplinary
Haining Ma, Zhengliang Lu, Xiang Zhang, Wenhe Liao
Summary: An improved strong tracking unscented Kalman filter based on multiplicative modified Rodrigues parameters (MRPs) is proposed for satellite attitude estimation in this paper. A novel method of weighted average is derived to maintain the multiplicative property of MRPs, with different procedures for Sigma points generation, state variables update, and covariance matrices calculation. Simulation results show excellent performance of the proposed filter under large attitude angles using raw telemetry data from CubeSat Enlai-1 in orbit.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Aerospace
Seid H. Pourtakdoust, M. Fakhari Mehrjardi, M. H. Hajkarim
Summary: A modified unscented Kalman filter is proposed in this paper to estimate the quaternion parameters and angular velocities of a gyro-less satellite under faulty sensor conditions. The filter demonstrates improved fault detection, sensor isolation, and attitude control performance, and copes well with sensor faults.
Article
Engineering, Aerospace
He Liao, Jinjin Xie, Xiaodong Zhou, Chuang Yao, Zhongxing Tang, Yanbin Zhao, Jirong Qi
Summary: This paper presents a compound control strategy for achieving attitude maneuver performance in the noncontact close-proximity formation satellite architecture. The strategy includes variable-parameter sliding mode control for the payload module and collision avoiding control with disturbance observer-based feedforward compensation to ensure synchronization of the separated modules within the small air clearance constraint of the noncontact Lorentz actuator. Experimental verification is conducted using a physical air-floating platform.
INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Fatemeh Sedghi, Mohammad Mehdi Arefi, Ali Abooee, Shen Yin
Summary: This article addresses and studies the problem of distributed finite-time consensus control for a class of stochastic nonlinear multiagent systems in the presence of various factors. Innovative control inputs are designed and proposed, and the stability and consensus of the system are proven.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Xiang Li, Yuchen Jiang, Minglei Li, Jiusi Zhang, Shen Yin, Hao Luo
Summary: This study combines the manual labeling process of doctors and introduces the correlation between single-modality and the tumor subcomponents into the segmentation network. The method improves the segmentation performance of brain tumors and can be applied in the clinical practice.
Article
Radiology, Nuclear Medicine & Medical Imaging
Minglei Li, Yuchen Jiang, Xiang Li, Shen Yin, Hao Luo
Summary: This study proposes an ensemble framework that combines three types of dedicatedly-designed convolutional neural networks (CNNs) and a multilayer perceptron (MLP) network to overcome the limitations of existing methods. Experimental results show that the proposed ensemble framework achieves superior performance under most evaluation metrics.
Article
Engineering, Electrical & Electronic
Jiusi Zhang, Jilun Tian, Minglei Li, Jose Ignaclo Leon, Leopoldo Garcia Franquelo, Hao Luo, Shen Yin
Summary: This article proposes a novel parallel hybrid neural network, consisting of 1-D convolutional neural network (1-DCNN) and bidirectional gated recurrent unit (BiGRU), for predicting remaining useful life (RUL). The spatial and temporal information from historical data is parallel extracted using 1-DCNN and BiGRU, respectively, and the trained network can be used for real-time RUL prediction. Experimental results demonstrate that the proposed parallel hybrid network effectively predicts RUL and outperforms existing literature.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Mechanical
Yiming Guo, Xiaojun Xing, Xiwei Wu, Cihang Wu, Bing Xiao
Summary: This paper investigates the problem of path-following control for parafoil dynamic systems in time-varying wind disturbance. An adaptive path-following controller is developed using the barrier Lyapunov function and backstepping method, considering the yaw rate constraint. The controller ensures the attenuation of disturbances caused by time-varying wind and modeling uncertainties, while avoiding violation of practical constraints on the yaw rate of the parafoil system. Experimental tests demonstrate the performance of the proposed path-following controller for parafoil systems.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Industrial
Jiusi Zhang, Xiang Li, Jilun Tian, Yuchen Jiang, Hao Luo, Shen Yin
Summary: Most supervised learning-based approaches assume that offline data and online data should have a similar distribution, which is difficult to satisfy in realistic remaining useful life prediction. To overcome this issue, a new transfer learning method called domain adaptation learning-oriented transfer learning (TL) is proposed. The method, called VLSTM-LWSAN, uses a local weighted deep sub-domain adaptation network to align fine-grained features between different degenerate stages, reducing the discrepancy between the target and source domains. Experimental results on an aircraft turbofan engine dataset demonstrate that VLSTM-LWSAN outperforms deep learning approaches without transfer learning and conventional transfer learning methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Automation & Control Systems
Mingyi Huo, Hao Luo, Hao Wang, Yuchen Jiang, Shen Yin, Okyay Kaynak
Summary: Due to informatization and the wider use of connected intelligence, modern industry has become large scale. To address the shortcomings of centralized design, this article proposes a subspace-aided distributed closed-loop monitoring approach that only utilizes local closed-loop subsystems' measurement information. The approach achieves distributed closed-loop monitoring by obtaining residuals of each subsystem through oblique projection technology and information interaction. The contributions of this article include equivalent monitoring to centralized design, reduced computational complexity, and secure information transmission.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Mingyi Huo, Hao Luo, Chao Cheng, Kuan Li, Shen Yin, Okyay Kaynak, Jiusi Zhang, Dejia Tang
Summary: This article proposes subspace-aided sensor fault diagnosis and compensation control approaches based on data-driven stable kernel representation (SKR) and stable image representation (SIR) identified by process data decompositions. The article obtains data-driven SKR and SIR through the mapping relationship of signal subspaces and presents a series of fault diagnosis and compensation approaches. Furthermore, an accurate online fault diagnosis and compensation approach is presented using online updating LQ decomposition for improved accuracy and timeliness. These approaches can diagnose, estimate, and compensate for multiple and different types of additive sensor faults. The effectiveness of the strategies has been verified through numerical study and experimentation with a three-tank system, demonstrating specific engineering significance.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Xiang Li, Songcen Lv, Minglei Li, Jiusi Zhang, Yuchen Jiang, Yong Qin, Hao Luo, Shen Yin
Summary: In this paper, a Spatial Dependence Multi-task Transformer (SDMT) network is proposed for 3D knee MRI segmentation and landmark localization. This method utilizes the shared encoder for feature extraction and uses spatial dependence to promote the two tasks. By adding spatial encoding to the features and designing a task hybrid multi-head attention mechanism, the proposed method effectively handles the spatial dependence between tasks and the correlation within a single task. Furthermore, a dynamic weight multi-task loss function is designed to balance the training process of the two tasks.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Engineering, Industrial
Muhammad Gibran Alfarizi, Federico Ustolin, Jorn Vatn, Shen Yin, Nicola Paltrinieri
Summary: Hydrogen is a clean alternative to hydrocarbon fuels in the marine industry. Liquid hydrogen can be used to transport and store large quantities of hydrogen. However, further research is needed to assess the potential risks and safety measures for this novel application.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Yunsong Xu, Zhengen Zhao, Shen Yin
Summary: This article investigates the performance optimization and fault tolerance of highly dynamic systems. An incremental control structure is proposed to optimize the performance by attaching a controller gain system to the predesigned controller. A structure integrating fault-tolerance strategy and hardware redundancy is also proposed to optimally fuse control commands from different control units.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Shimeng Wu, Hao Luo, Yuchen Jiang, Jiusi Zhang, Jilun Tian, Shen Yin
Summary: This article proposes a data-driven unsupervised defense scheme for nonlinear systems, which decomposes data into two subspaces and achieves secure transmission by hiding dynamic-related information and transmitting dynamic-independent information plaintext. The scheme can detect both nonstealthy and stealthy attacks simultaneously, and comparative experiments demonstrate its high detection accuracy and excellent encryption capability.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Qi Liu, Jianxun Li, Shuping Ma, Shen Yin, Baoping Jiang, Chunyu Yang
Summary: This article addresses the challenge of actuator attacks in delayed singular semi-Markov jump systems with uncertainties and exogenous disturbances. It introduces a random switch surface to stabilize systems against actuator attacks and establishes H-infinity stochastic admissibility sufficient conditions under partially unknown transition rate and completely unknown transition rate matrices. Two algorithms using linear matrix inequalities are presented to determine the gain matrices, and an innovative adaptive H-infinity neural sliding mode control law is conducted to approximate actuator attacks and estimate unavailable parameter bounds in real-time.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Minglei Li, Hang Zhou, Xiang Li, Pengfei Yan, Yuchen Jiang, Hao Luo, Xianli Zhou, Shen Yin
Summary: Early detection and accurate identification of thyroid nodules are challenging tasks. This study proposes a novel end-to-end network equipped with a deformable attention network and a distillation-driven interaction aggregation module (DIAM) to improve the diagnosis and interpretability of thyroid nodules. Experimental results show competitive performance and clinical interpretability.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2023)
Article
Computer Science, Theory & Methods
Shimeng Wu, Hao Luo, Shen Yin, Kuan Li, Yuchen Jiang
Summary: This paper proposes a residual-driven comprehensive defense scheme based on the coprime factorization technique to address the threat posed by concealed CPS attacks. The novel scheme protects CPS from stealth cyber-physical attacks through secure transmission and attack detection, which are validated through simulation research on the F-404 engine.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
