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第35届中国过程控制会议

作者： Xuqing Liang Chunjie Zhou Yu-Chu Tian Xin Du Peihang Xu School of Artificial Intelligence and Automation Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Computer Science Queensland University of Technology

The safety protection of process control systems plays a crucial role in the overall safety of critical *** have increased the complexity of existing safety protection analysis. Traditional safety analysis methods fall short in accounting for cyberattack factors, making it challenging to conduct safety protection analysis under cyberattacks. To address this issue, this paper presents a new safety protection analysis method that considers multiple safety factors explicitly including cyberattacks using formal verification. The method consists of three main components: exhaustive system safety specifications,formal models, and system safety validation. The system safety specification component adds a cyberattack factor to system safety requirements based on the system theory process analysis(STPA) method. The formal model component considers the system's dynamic operation process, and safety protection behaviors under typical attack behaviors. The system safety validation component validates the effectiveness of system safety protection under cyberattacks by the UPPAAL tool, from the perspective of whether system safety constraints are triggered and whether the change curve of process variables is compliant. Finally, the effectiveness of the presented approach is carried out for a simplified fluid catalytic cracking(FCC) fractionating system.

关键词： Safety protection analysis Formal verification Cyberattacks System theory process analysis(STPA)

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Gait Planning and Motion control Based on Vrep Simulation for Quadruped Robot 5

Gait Planning and Motion Control Based on Vrep Simulation fo...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

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Single-Loop Deadbeat Predictive control Framework for Uncertain SPMSM Drives With Disturbance Observation

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IEEE Transactions on Transportation Electrification 2025年

作者： Jiang, Fuxi Wang, Siqi Guo, Yuhao Cheng, Zhe Ye, Jie Cheng, Shanmei Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology School of Optical and Electronic Information Wuhan430074 China

To mitigate the detrimental effects of parameter perturbations and external load torque on the accuracy of speed and current tracking control in surface-mounted permanent magnet synchronous motor (SPMSM) drive systems, while avoiding the complexities inherent in traditional speed-current series control architectures, this paper presents a single-loop deadbeat predictive control (SDPC) framework that incorporates online disturbance observation. The control input voltage vector is directly computed using an SDPC controller in conjunction with a set of discrete-time disturbance observers (DDOBs). This proposed methodology offers two notable advantages over existing research: (i) it substantially reduces the number of adjustable controller parameters, requiring only the tuning of the observer bandwidth and the speed scaling gain, which enhances its practicality for engineering applications;and (ii) the disturbance estimation and compensation mechanism utilizing DDOBs significantly improves the robustness of the SPMSM system. Furthermore, the SDPC framework ensures that the system maintains satisfactory transient and steady-state tracking performance in response to reference signals. Experimental comparisons validate both the effectiveness and superiority of the proposed method. © 2015 IEEE.

关键词： Permanent magnets

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Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces

arXiv

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arXiv 2025年

作者： Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China

Due to the non-stationarity and large individual differences of EEG signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/session, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 10 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits the EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding. © 2025, CC BY.

关键词： Calibration

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When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo

When Epipolar Constraint Meets Non-local Operators in Multi-...

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International Conference on Computer Vision (ICCV)

作者： Tianqi Liu Xinyi Ye Weiyue Zhao Zhiyu Pan Min Shi Zhiguo Cao Ministry of Education School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Learning-based multi-view stereo (MVS) method heavily relies on feature matching, which requires distinctive and descriptive representations. An effective solution is to apply non-local feature aggregation, e.g., Transformer. Albeit useful, these techniques introduce heavy computation overheads for MVS. Each pixel densely attends to the whole image. In contrast, we propose to constrain nonlocal feature augmentation within a pair of lines: each point only attends the corresponding pair of epipolar lines. Our idea takes inspiration from the classic epipolar geometry, which shows that one point with different depth hypotheses will be projected to the epipolar line on the other view. This constraint reduces the 2D search space into the epipolar line in stereo matching. Similarly, this suggests that the matching of MVS is to distinguish a series of points lying on the same line. Inspired by this point-toline search, we devise a line-to-point non-local augmentation strategy. We first devise an optimized searching algorithm to split the 2D feature maps into epipolar line pairs. Then, an Epipolar Transformer (ET) performs non-local feature augmentation among epipolar line pairs. We incorporate the ET into a learning-based MVS baseline, named ET-MVSNet. ET-MVSNet achieves state-of-the-art reconstruction performance on both the DTU and Tanks-and-Temples benchmark with high efficiency. Code is available at https://***/TQTQliu/ET-MVSNet.

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MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition

arXiv

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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Gao, Changxin Zhang, Yingya Zhao, Deli Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China

Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force long-range temporal perception;ii) explicit motion learning is usually ignored, leading to partial information loss. To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder. Specifically, the long-short contrastive objective is to endow local frame features with long-form temporal awareness by maximizing their agreement with the global token of videos belonging to the same class. The motion autodecoder is a lightweight architecture to reconstruct pixel motions from the differential features, which explicitly embeds the network with motion dynamics. By this means, MoLo can simultaneously learn long-range temporal context and motion cues for comprehensive few-shot matching. To demonstrate the effectiveness, we evaluate MoLo on five standard benchmarks, and the results show that MoLo favorably outperforms recent advanced methods. The source code is available at https://***/alibaba-mmai-research/MoLo. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Neural-Network-Based Nonlinear Model Predictive Tracking control of a Pneumatic Muscle Actuator-Driven Exoskeleton

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IEEE/CAA Journal of Automatica Sinica 2020年第6期7卷 1478-1488页

作者： Yu Cao Jian Huang IEEE Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Pneumatic muscle actuators(PMAs)are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries,such as strokes,spinal cord injuries,etc.,to accomplish rehabilitation ***,because PMAs have nonlinearities,hysteresis,and uncertainties,etc.,complex mechanisms are rarely involved in the study of PMA-driven robotic *** this paper,we use nonlinear model predictive control(NMPC)and an extension of the echo state network called an echo state Gaussian process(ESGP)to design a tracking controller for a PMA-driven lower limb *** dynamics of the system include the PMA actuation and mechanism of the leg orthoses;thus,the system is represented by two nonlinear uncertain *** facilitate the design of the controller,joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP.A gradient descent algorithm is employed to solve the optimization problem and generate the control *** stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system *** and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.

关键词： Echo state Gaussian process model predictive control neural network pneumatic muscle actuators-driven exoskeleton

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Demand-Side Frequency control in Power Network Based on Multi-Agent Deep Reinforcement Learning

Demand-Side Frequency Control in Power Network Based on Mult...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Xianggang Liu Zhi-Wei Liu Ming Chi Yifan Zhang Key Laboratory of Image Processing and Intelligent Control Ministry of Education College of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

The integration of renewable energy sources into the power grid has led to new challenges in maintaining the stability of the system frequency. This paper proposes a novel approach to address the Optimal Demand Side Frequency control (ODFC) problem using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method. The proposed method models the ODFC problem as a Markov game, with centralized training based on multi-agent cooperative self-learning and associative storage service. In the decentralized execution stage, each agent independently outputs control actions to the controlled plant using local observations. Numerical simulations show that the proposed method effectively addresses the ODFC problem with superior performance compared to traditional methods.

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Semi-Supervised Generalized Source-Free Domain Adaptation (SSG-SFDA)

Semi-Supervised Generalized Source-Free Domain Adaptation (S...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiayu An Changming Zhao Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Continual learning aims to learn on a sequence of new tasks while maintaining the performance on previous tasks. Source-free domain adaptation (SFDA), which adapts a pretrained source model to a target domain, is useful in protecting the source domain data privacy. Generalized SFDA (G-SFDA) combines continual learning and SFDA to achieve outstanding performance on both the source and the target domains. This paper proposes semi-supervised G-SFDA (SSG-SFDA) for domain incremental learning, where a pre-trained source model (instead of the source data), few labeled target data, and plenty of unlabeled target data, are available. The goal is to achieve good performance on all domains. To cope with domain-ID agnostic, SSG-SFDA trains a conditional variational auto-encoder (CVAE) for each domain to learn its feature distribution, and a domain discriminator using virtual shallow features generated by CVAE to estimate the domain ID. To cope with catastrophic forgetting, SSG-SFDA uses soft domain attention to improve the sparse domain attention in G-SFDA. To cope with insufficient labeled target data, SSG-SFDA uses MixMatch to augment the unlabeled target data and better exploit the few labeled target data. Experiments on three datasets demonstrated the effectiveness of SSG-SFDA.

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DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform

arXiv

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arXiv 2024年

作者： Zhu, Huizhi Xu, Wenxia Huang, Jian The Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

In this paper, we propose a graph neural network, DisGNet, for learning the graph distance matrix to address the forward kinematics problem of the Gough-Stewart platform. DisGNet employs the k-FWL algorithm for message-passing, providing high expressiveness with a small parameter count, making it suitable for practical deployment. Additionally, we introduce the GPU-friendly Newton-Raphson method, an efficient parallelized optimization method executed on the GPU to refine DisGNet’s output poses, achieving ultra-high-precision pose. This novel two-stage approach delivers ultra-high precision output while meeting real-time requirements. Our results indicate that on our dataset, DisGNet can achieves error accuracys below 1mm and 1deg at 79.8% and 98.2%, respectively. As executed on a GPU, our two-stage method can ensure the requirement for real-time computation. Codes are released at https://***/FLAMEZZ5201/DisGNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Newton-Raphson method

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