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第42届中国控制会议

作者： Jiukai Zhu Wudong Deng Leixiang Sun Yanning Guo Department of Control Science and Engineering Harbin Institute of Technology Shanghai Institute of Satellite Engineering

A fuel-optimal spacecraft rendezvous mission planning method considering orbital perturbation is proposed to address the fuel consumption problem of spacecraft completing rendezvous ***,considering the constraints of total mission time and desired position,a model of orbital perturbation spacecraft considering non-spherical and atmospheric drag is constructed,and the adaptation function of fuel consumption for single-loop Lambert transfer considering orbital perturbation is *** on this fitness function,an improved particle swarm optimization algorithm is designed to determine the first applied impulse time,the second applied impulse time,and the rendezvous time in the rendezvous mission planning,and then solve the two applied impulse sizes with the minimum fuel consumption to complete the spacecraft rendezvous ***,simulation experiments with a set of spacecraft rendezvous missions are conducted to verify the practicality and effectiveness of the method.

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Robust Model Predictive control of Position Sensorless Driven IPMSM Based on Cascaded EKF-LESO

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IEEE Transactions on Transportation Electrification 2025年

作者： Xu, Ruiqi Shen, Xiaoning Lin, Xinpo Liu, Zhuang Xu, Dezhi Liu, Jianxing Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Southeast University School of Electrical Engineering Engineering Research Center of Electrical Transport Technology Ministry of Education Nanjing210096 China

This paper proposed a robust model predictive control for permanent magnet synchronous motors taking into account the effect of disturbance. A structure cascading extended Kalman filter (EKF) and linear extended state observer (LESO) is used to estimate the position of the motor without using encoders. Firstly, the voltage vector reference containing information of the system speed and current are predicted using the deadbeat control. Meanwhile, the ultra-local model of the system is built considering the lumped disturbance of the motor. The disturbed part of the ultra-local model is updated online according to the information of voltage and current in past two moments. Then, a cascaded EKF-LESO is designed to efficiently estimate position and speed, taking into account disturbances such as measurement noise and model parameter uncertainty. Experimental results verify the effectiveness and advantages of the proposed method. © 2015 IEEE.

关键词： Predictive control systems

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Extremum seeking in the presence of large delays via time-delay approach to averaging

arXiv

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arXiv 2023年

作者： Yang, Xuefei Fridman, Emilia Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China School of Electrical Engineering Tel-Aviv University Israel

In this paper, we study gradient-based classical extremum seeking (ES) for uncertain n-dimensional (nD) static quadratic maps in the presence of known large constant distinct input delays and large output constant delay with a small time-varying uncertainty. This uncertainty may appear due to network-based measurements. We present a quantitative analysis via a time-delay approach to averaging. We assume that the Hessian has a nominal known part and norm-bounded uncertainty, the extremum point belongs to a known box, whereas the extremum value to a known interval. By using the orthogonal transformation, we first transform the original static quadratic map into a new one with the Hessian containing a nominal diagonal part. We apply further a time-delay transformation to the resulting ES system and arrive at a time-delay system, which is a perturbation of a linear time-delay system with constant coefficients. Given large delays, we choose appropriate gains to guarantee stability of this linear system. To find a lower bound on the dither frequency for practical stability, we employ variation of constants formula and exploit the delay-dependent positivity of the fundamental solutions of the linear system with their tight exponential bounds. Sampled-data ES in the presence of large distinct input delays is also presented. Explicit conditions in terms of simple scalar inequalities depending on tuning parameters and delay bounds are established to guarantee the practical stability of the ES control systems. We show that given any large delays and initial box, by choosing appropriate gains we can achieve practical stability for fast enough dithers and small enough uncertainties. Copyright © 2023, The Authors. All rights reserved.

关键词： Time delay

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Exploring adversarial robustness of deep state space models 24

Exploring adversarial robustness of deep state space models

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Biqing Qi Yiang Luo Junqi Gao Pengfei Li Kai Tian Zhiyuan Ma Bowen Zhou Department of Electronic Engineering Tsinghua University and Shanghai Artificial Intelligence Laboratory Department of Control Science and Engineering Harbin Institute of Technology School of Mathematics Harbin Institute of Technology Department of Electronic Engineering Tsinghua University

ISBN: (纸本)9798331314385

Deep State Space Models (SSMs) have proven effective in numerous task scenarios but face significant security challenges due to Adversarial Perturbations (APs) in real-world deployments. Adversarial Training (AT) is a mainstream approach to enhancing Adversarial Robustness (AR) and has been validated on various traditional DNN architectures. However, its effectiveness in improving the AR of SSMs remains unclear. While many enhancements in SSM components, such as integrating Attention mechanisms and expanding to data-dependent SSM parameterizations, have brought significant gains in Standard Training (ST) settings, their potential benefits in AT remain unexplored. To investigate this, we evaluate existing structural variants of SSMs with AT to assess their AR performance. We observe that pure SSM structures struggle to benefit from AT, whereas incorporating Attention yields a markedly better trade-off between robustness and generalization for SSMs in AT compared to other components. Nonetheless, the integration of Attention also leads to Robust Overfitting (RO) issues. To understand these phenomena, we empirically and theoretically analyze the output error of SSMs under AP. We find that fixed-parameterized SSMs have output error bounds strictly related to their parameters, limiting their AT benefits, while input-dependent SSMs may face the problem of error explosion. Furthermore, we show that the Attention component effectively scales the output error of SSMs during training, enabling them to benefit more from AT, but at the cost of introducing RO due to its high model complexity. Inspired by this, we propose a simple and effective Adaptive Scaling (AdS) mechanism that brings AT performance close to Attention-integrated SSMs without introducing the issue of RO. Our code is available at https://***/Biqing-Qi/***.

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Extremum Seeking for Single-Variable Static Map with Measurement Noise via A Time-Delay Approach

Extremum Seeking for Single-Variable Static Map with Measure...

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IEEE International Conference on Cyber technology in Automation, control, and Intelligent Systems

作者： Bowen Zhao Xuefei Yang Emilia Fridman Bin Zhou Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China School of Electrical Engineering Tel-Aviv University Israel

In this article, we introduce a time-delay approach to extremum seeking (ES) for scalar static quadratic maps with measurement noise. The approach involves transforming the ES system into a time-delay neutral type system with stochastic perturbations, which guarantees the stability of the original ES system. Using a Lyapunov-Krasovskii (L-K) method, explicit conditions are established for the mean-square ultimate boundedness of the ES control systems. These conditions are expressed in terms of LMIs and depend on the intensity of measurement noise, tuning parameters, and a known arbitrarily large constant $L$ that bounds the 6th moment of the estimation error. Compared to existing results for ES with measurement noise obtained via qualitative analysis, our approach provides a quantitative analysis via a time-delay approach to averaging. A numerical example is provided to illustrate the efficiency of the proposed method.

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Error-driven Tracking control Design with Preset-input LMS Adaptive Filter 11

Error-driven Tracking Control Design with Preset-input LMS A...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Liu, Qingquan Huo, Xin Liu, Kangzhi Zhao, Hui Wu, Aijing Control and Simulation Center Harbin Institute of Technology Harbin150080 China Chiba University Department of Electrical and Electronic Engineering Chiba263-8522 Japan

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

There is a problem of insufficient reference tracking accuracy in servo system driven by permanent magnet synchronous motor (PMSM), which limits its application. This paper aims to propose a modified error-driven preset-input least mean square adaptive filter (EPLMS-AF) to improve the tracking accuracy. The reference tracking error is used to drive the update of adaptive weights in EPLMS-AF. The designed preset-inputs makes the reference tracking error converge to zero according to the form of reference. On the premise of choosing an appropriate iteration size to ensure the stability of the filter, the tracking error is able to reduce to zero along the gradient direction. At last, numerical simulations are provided to illustrate the high efficiency and wide applicability of the proposed method. © 2022 IEEE.

关键词： Errors

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Deep attention convolutional neural network-based adaptive multi-source information fusion for accurate short-term photovoltaic power forecast

Deep attention convolutional neural network-based adaptive m...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Mingliang Bai Yunxiao Chen Zhihao Zhou Zhenhua Long Jinfu Liu Daren Yu Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Energy Science and Engineering Harbin Institute of Technology Harbin China

Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) power generation is currently one of the most important ways to utilize solar energy. PV power has strong uncertainties and is adverse to the stability of the electricity grid. PV power forecast is an important method to tackle this issue. Conventional methods only use historical PV power data. This paper proposes a new method that uses deep Convolutional Neural Networks (CNN)-based multivariable information fusions for PV forecast. Multiple variables including global horizontal irradiation (GHI), relative humidity, air temperature, cloud thickness, wind speed, etc. are used for PV forecast. Meanwhile, clear-sky GHI estimated by McClear clear-sky models are used as physical prior knowledge. Through the attention mechanism, these variables are adaptively fused with the historical PV power data to realize an accurate PV power forecast. Forecast experiments in three-year (2017-2019) actual data of Brussels PV power stations verify the significant superiority of the proposed method over conventional forecast methods.

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High biomass yields of Chlorella protinosa with efficient nitrogen removal from secondary effluent in a membrane photobioreactor

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Journal of Environmental Sciences 2024年第12期146卷 272-282页

作者： Min Yang Zhen Liu Aijie Wang Ingmar Nopens Hairong Hu Hong Chen School of Civil and Environmental Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province Changsha 410114China BIOMATH Department of Data Analysis and Mathematical ModellingGhent UniversityGent B 9000Belgium Key Laboratory of Environmental Biotechnology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China School of Materials Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Further treatment of secondary effluents before their discharge into the receiving water bodies could alleviate water *** this study,the Chlorella proteinosa was cultured in a membrane photobioreactor to further remove nitrogen from the secondary *** effect of hydraulic retention time(HRT)on microalgae biomass yields and nutrient removal was *** results showed that soluble algal products concentration reduced in the suspension at low HRT,thereby alleviating microalgal growth *** addition,the lower HRT reduced the nitrogen limitation for Chlorella proteinosa’s growth through the phase-out of nitrogen-related functional *** a result,the productivity for Chlorella proteinosa increased from 6.12 mg/L/day at an HRT of 24 hr to 20.18 mg/L/day at an HRT of 8 *** highest removal rates of 19.7 mg/L/day,23.8 mg/L/day,and 105.4 mg/L/day were achieved at an HRT of 8 hr for total nitrogen(TN),ammonia,and chemical oxygen demand(COD),***,in terms of removal rate,TN and COD were the largest when HRT is 24 hr,which were 74.5%and 82.6%*** maximum removal rate of ammonia nitrogen was 99.2%when HRT was 8 hr.

关键词： Chlorella MPBR Fouling HRT SAP Nutrient

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Spatial-Temporal Graph U-Net for Skeleton-Based Human Motion Infilling

Spatial-Temporal Graph U-Net for Skeleton-Based Human Motion...

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IEEE International Conference on Industrial technology (ICIT)

作者： Leiyang Xu Qiang Wang Chenguang Yang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Motion infilling is a fundamental and challenging research field in human motion modeling and analysis, which aims to generate natural and visually coherent transitions to fill in missing motion frames based on the start and end motion sequences. However, most current methods ignore the spatial structure formed by joints, which may lose some spatial information. This work proposes a novel spatiotemporal graph U-Net that supports flexible inputs for skeleton-based motion infilling. We apply spatiotemporal graph convolutional layers, skeleton pooling layers, and skeleton unpooling layers to extract spatial and temporal features in the motion sequence. At the same time, we use the U-Net structure to integrate the information in the start and end motion sequences. In addition, the generative adversarial mechanism is introduced to ensure the generated skeleton poses are smooth and natural. We conduct experiments on two motion datasets, including one large-scale public dataset and one self-built dataset. The model inputs are joint quaternions or joint coordinates. Experimental results show that our method can improve the performance of skeleton-based motion infilling and achieve state-of-the-art results when using joint coordinates as model input.

关键词： Analytical models Quaternions Feature extraction Skeleton Data models Spatiotemporal phenomena

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STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 226-238页

作者： Zebing Wei Hongxia Zhao Zhishuai Li Xiaojie Bu Yuanyuan Chen Xiqiao Zhang Yisheng Lv Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the Shandong Key Laboratory of Smart Transportation(Preparation) Shandong Jiao Tong UniversityShanghai 200240China the College of Science China University of PetroleumQingdao 266580China the Department of Transportation Engineering School of Transportation Science and TechnologyHarbin Institute of TechnologyHarbin 150090China IEEE

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the *** existing frameworks typically utilize separate modules for spatial and temporal correlations ***,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some ***,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between *** overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies ***,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution *** each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting ***,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven ***,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global *** projects the embedding input repeatedly with multiple different ***,the predicted values are generated by stacking several multi-stream *** experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA

关键词： Deep learning graph neural network(GNN) multistream spatial-temporal feature extraction temporal graph traffic prediction

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