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Stabilization With Prescribed inst.nt via Lyapunov Method

IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 557-559页

作者： Jiyuan Kuang Yabin Gao Yizhuo Sun Aohua Liu Jianxing Liu Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China

Dear Editor,This letter presents a prescribed-inst.nt stabilization approach to high-order integrator systems by the Lyapunov method. Under the presented controller, the settling time of controlled systems is independ... 详细信息

关键词： method. prescribed inst.nt

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mVLINS: A Multilevel Visual-LiDAR-Inertial Navigation System with Completely Decoupled Odometry and Adaptive Environmental Mapping

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Zhang, Bing Yao, Weiran Wang, Yankun Li, Peng Shao, Xiangyu Sun, Guanghui department of control science and engineering Harbin Institute of Technology Harbin China

The core component of the navigation system is the process of multi-sensor fusion localization in a challenging environment with limited GNSS constraints. This process is designed to robustly and precisely estimate the system state and map the traversed area. In this paper, we propose a low-drift, highly resilient multilevel navigation system with completely decoupled odometry and adaptive environmental mapping. A novel method for visual-LiDAR-inertial frame-to-frame odometry is presented to leverage the complementary strengths of these sensors. This odometry approach involves decoupling the 6-degree-of-freedom (6-DoF) state to allocate each state component to an appropriate submodule for estimation. An adaptive environmental mapping module that aims to align the target frame with the local map is proposed to refine the rough odometry pose. This module is achieved through the utilization of an adaptive keyframe strategy and the feature consistency constraint. Enhance the matching of keyframes to the map by reducing the point-to-feature error between frame points and map features. Additionally, the state is further refined by minimizing the feature consistency error on lines in the adjacent corner map and the local map at each keyframe. Our proposed algorithm is validated using both public and self-collected datasets, demonstrating superior results compared to state-of-the-art algorithms. IEEE

关键词： Optical radar

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Fast fixed-time three-dimensional terminal guidance with non-concave trajectory constraint

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Chinese Journal of Aeronautics 2024年第3期37卷 208-218页

作者： Youmin GONG Yanning GUO Yueyong LYU Dongyu LI Guangfu MA Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China School of Cyber Science and Technology Beihang UniversityBeijing 100191China

Focusing on the non-concave trajectory constraint,a sliding-mode-based nonsingular feedback fast fixed-time three-dimensional terminal guidance of rotor unmanned aerial vehicle landing,planetary landing and spacecraft rendezvous and docking terminal phase with external disturbance is investigated in this ***,a fixed-time observer based on real-time differentiator is developed to compensate for the external disturbance,whose estimation error can converge to zero after a time independent of the initial ***,a sliding surface ensuring fixed-time convergence is *** sliding surface can guarantee that the vehicle achieves a non-concave trajectory,which is better for avoiding collision and maintaining the visibility of the landing site or docking ***,the nonsingular guidance ensuring the fixed-time convergence of the sliding surface is proposed,which is continuous and chatter *** last,three numerical simulations of Mars landing are performed to validate the effectiveness and correctness of the designed scheme.

关键词： Three-dimensional Terminal guidance Non-concave trajectory constraint Fast fixed-time control Sliding mode control

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Model-Free Current Predictive control for PMSMs With Ultralocal Model Employing Fixed-Time Observer and Extremum-Seeking Method

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IEEE Transactions on Power Electronics 2025年第8期40卷 10682-10693页

作者： Lin, Xinpo Liu, Jianxing Liu, Zhuang Gao, Yabin Peretti, Luca Wu, Ligang Harbin Institute of Technology Department of Control Science and Engineering Weihai264209 China Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China KTH Royal Institute of Technology Division of Electric Power and Energy Systems Stockholm10044 Sweden

Conventional model predictive current control of permanent magnet synchronous machines (PMSMs) relies heavily on a precise mathematical model, which may be challenging to obtain in certain cases. To address this issue, this article proposes a model-free predictive current controller for PMSMs. Specifically, the dynamic model of the motor currents is represented using two ultralocal models, which express the derivatives of a controlled output as the sum of an amplified control input and an unknown offset term. All system parameters, nonlinear terms, and unmodeled dynamics are encapsulated into two offset terms. Online estimation of these two offset terms is performed using a second-order fixed-time convergence observer without requiring exact knowledge of the system parameters in advance. In contrast to both the linear extended state observer and the super-twisting observer, the fixed-time observer enhances convergence speed while concurrently sustaining minimal chatting. To get rid of the need for model rated parameters, an extremum-seeking approach is utilized to tune the control gains in the ultralocal model, where the amplitude of the disturbance item in extremum-seeking approach is adaptively attenuated, resulting in effective reduction of the fluctuations of both the control gains and PMSM's currents in steady state. Subsequently, a predictive controller is designed using this ultralocal model. Finally, the effectiveness and advantages of the proposed control scheme are confirmed through experimental results. © 1986-2012 IEEE.

关键词： Permanent magnets

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Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5805-5820页

作者： Qi, Biqing Gao, Junqi Chen, Xinquan Li, Dong Liu, Jianxing Wu, Ligang Zhou, Bowen Tsinghua University Department of Electronic Engineering Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Harbin Institute of Technology Department of Control Science and Engineering Harbin China Harbin Institute of Technology School of Mathematics Harbin China

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounters additional challenges. The scarcity of samples in new sessions intensifies overfitting, causing incompatibility between the output features of new and old classes, thereby escalating catastrophic forgetting. A prevalent strategy involves mitigating catastrophic forgetting through the Explicit Memory (EM), which comprise of class prototypes. However, current EM-based methods retrieves memory globally by performing Vector-to-Vector (V2V) interaction between features corresponding to the input and prototypes stored in EM, neglecting the geometric structure of local features. This hinders the accurate modeling of their positional relationships. To incorporate information of local geometric structure, we extend the V2V interaction to Graph-to-Graph (G2G) interaction. For enhancing local structures for better G2G alignment and the prevention of local feature collapse, we propose the Local Graph Preservation (LGP) mechanism. Additionally, to address sample scarcity in classes from new sessions, the Contrast-Augmented G2G (CAG2G) is introduced to promote the aggregation of same class features thus helps few-shot learning. Extensive comparisons on CIFAR100, CUB200, and the challenging ImageNet-R dataset demonstrate the superiority of our method over existing methods. © 1991-2012 IEEE.

关键词： Prototypes Adaptation models Power capacitors Feature extraction Data models Accuracy Vectors Training Incremental learning Overfitting

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Influencing factor analysis of interception probability and classification-regression neural network based estimation

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Journal of Systems engineering and Electronics 2023年第4期34卷 992-1006页

作者： NAN Yi YI Guoxing HU Lei WANG Changhong TU Zhenbiao Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Beijing Electro-mechanical Engineering Institute Beijing 100074China

The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually *** calculation methods have very limited analysis of the influence mechanism of influencing factors,and none of them has analyzed the influence of the guidance *** paper considers the influencing factors of both the interceptor and the target more *** parameters include speed,guidance law,guidance error,fuze error,and fragment killing ability,while target performance includes speed,maneuverability,and *** this paper,an interception model is established,Monte Carlo simulation is carried out,and the influence mechanism of each factor is analyzed based on the model and simulation ***,this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing *** proposed method reduces the interference of invalid interception data to valid data,so its prediction accuracy is significantly better than that of pure regression neural networks.

关键词： interception probability simulation modeling analysis of influencing factors probability estimation neural networks

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Prescribed-Time Three-Dimensional Guidance for Maneuvering Targets with Field-of-View and Approach Angle Constraints

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 5996-6010页

作者： Kang, Honglong Wang, Pengyu Song, Shenmin Fonod, Robert Harbin Institute of Technology Center for Control Theory and Guidance Technology Harbin150001 China Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Ecole Polytechnique Fédérale de Lausanne School of Architecture Civil and Environmental Engineering LausanneCH-1015 Switzerland

This paper presents a three-dimensional (3D) guidance law for homing missiles to intercept maneuvering targets, while ensuring a desired final approach angle and satisfying field-of-view (FOV) constraints. First, a line-of-sight (LOS) vector shaping method is introduced in the range domain. The desired approach angle and FOV constraint can be satisfied by precisely tracking a specified LOS vector profile. Then, a feedback guidance law is designed using a prescribed-time control technique for nullifying the LOS tracking errors. This guidance law employs an extended state observer to estimate and compensate for unknown target maneuvers, with a rigorous analysis conducted to ensure the stability of the entire system. Unlike existing studies, the proposed guidance law does not require linearization procedures and switching logic, which avoids discontinuous guidance command and facilitates practical implementation. Finally, extensive numerical simulations and comparison studies validate the effectiveness, robustness, and superiority of the proposed method. © 1965-2011 IEEE.

关键词： Guided missiles

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Facial Expression Recognition via Closed-Loop Transcription 50

Facial Expression Recognition via Closed-Loop Transcription

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Liu, Xuan Ma, Jiachen Wang, Qiang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Control Science and Engineering Harbin Institute of Technology Weihai China

ISBN: (纸本)9781665464543

Convolutional Neural Networks (CNNs) are the most widely used and successful approaches for accomplishing the Facial Expression Recognition (FER) task. However, these technologies appear to have reached a bottleneck since the networks are becoming increasingly complex and heavier, yet the performance improvements remain minimal. The "black box" nature of CNNs makes it challenging to identify and address this crux. This paper aims to address the issue by utilizing the principles of data compression and discriminative representation within a plausible theoretical framework. We are employing an innovative computational framework, drawing inspiration from feedback loop control systems, to develop an interpretable closed-loop transcription model to generate a Linear Discriminative Representation (LDR) for multi-class real-world datasets. This model, comprised of an encoder and decoder that facilitates data mapping between the data space and the feature space, employs the output of the transcription system as feedback or guidance, with the objective of improving the accuracy and efficiency of the transcription process. The optimal LDR is learned jointly for all data classes through a two-player minimax game between the encoder and decoder for the learned representation over a single rate reduction-based objective. The studies conducted on the FER2013 dataset show promising results for the proposed closed-loop formulation. The visual quality of the learned decoder and the classification performance of the encoder are comparable to those of large and complex networks. An accuracy of 70.97% was attained on the FER2013 dataset by applying Principal Component Analysis (PCA) to the learned representation. © 2024 IEEE.

关键词： Decoding

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Event-based sliding mode control under denial-of-service attacks

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science China(Information sciences) 2022年第6期65卷 125-141页

作者： Yingxin TIAN Xiang LI Bo DONG Yabin GAO Ligang WU Department of Control Science and Engineering Harbin Institute of Technology School of Software Harbin Institute of Technology

This paper deals with the problem of estimator-based sliding mode control against.denial-of-service(DoS) attacks and discrete events via a time-delay approach. A networked system is considered an uncertain dynamical system with matched and mismatched perturbations and exogenous disturbance in the network environment. A network-resource-aware event-triggering mechanism is designed with aperiodically releasing system measurements. Furthermore, to describe the DoS attack duration and inter-event time, a time-delay modeling approach considers the DOS attack duration and inter-event time as a “time delay” of the measurements between the sensor and controller over the network is proposed. Consequently, an intervaltime-delay system with uncertainties is formulated. A state-observer-based sliding mode controller, by which the ideal sliding mode can be achieved, is proposed against.the DoS attacks. The resulting sliding motion is proved to be robust and stable with an Lgain performance. Finally, the effectiveness and applicability of the present sliding mode control are validated in a simulated pendulum system.

关键词： sliding mode control time delay uncertain system state observer event trigger

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An improved ultrasound high-resolution imaging method based on spatially-variant model 22

An improved ultrasound high-resolution imaging method based ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Gao, Jie Chen, Yifei Li, Xiangyu Zhang, Xin Liu, Jian Shen, Yi Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Instrument Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798331527471

Ultrasound high-resolution imaging is essential in clinic. The limited number of channels in portable ultrasound machines results in low-resolution images being acquired. Achieving high-resolution ultrasound images relies on machines with a substantial number of channels, which increases costs. Obtaining potential high-channel images from low-channel images can significantly reduce the cost of ultrasound machines. A novel physics-based deep learning method spatially-variant model (SV-Net) is proposed to deconvolve low-channel ultrasound images, yielding high-resolution outputs. SV-Net is structured with a multi-channel Wiener deconvolution layer placed before Convolutional Neural Network (CNN). The multichannel Wiener deconvolution layer consists of various differentiable Wiener deconvolutions, which leverage knowledge of spatially-variant Point Spread Functions (PSFs) in ultrasound images. These PSFs are subsequently optimized through further training to obtain high-quality ultrasound images. Experiments show the method’s efficacy in improving lateral resolution and achieving favorable performance metrics, including PSNR, SSIM, MAE, and FWHM. © 2024 IEEE.

关键词： Ultrasonic imaging

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