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Globally Composite-Learning-Based Intelligent Fast Finite-Time control for Uncertain Strict-Feedback Systems with Nonlinearly Periodic Disturbances

arXiv

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arXiv 2023年

作者： Wang, Xidong Li, Zhan He, Zhen The Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China The Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

This brief aims at the issue of globally composite-learning-based neural fast finite-time (F-FnT) tracking control for a class of uncertain systems in strict-feedback form subject to nonlinearly periodic disturbances. First, uncertain dynamics with periodic parameters are identified by incorporating Fourier series expansion (FSE) into an intelligent estimator, which leverages the feedback of newly designed prediction errors in updating weights to boost learning performance. Then, a novel switching mechanism is constructed to fulfill smooth switching from the composite FSE-based neural controller to robust control law when the inputs of the intelligent estimator transcend the valid approximation domain. By fusing the switching mechanism with an improved F-FnT backstepping algorithm, the globally F-FnT boundedness of all variables in the closed-loop system is guaranteed. Finally, a simulation study is conducted to evince the availability of the theoretical result. Copyright © 2023, The Authors. All rights reserved.

关键词： Backstepping

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A NTSMCLF Attitude Tracking controller for the Satellite with Flexible Accessories Based on the Stewart platform

A NTSMCLF Attitude Tracking Controller for the Satellite wit...

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Chinese control Conference (CCC)

作者： Xixuan Chen He Zhu Junxiong Yin Yumeng Han Yanning Guo Department of Control Science and Engineering Harbin Institute of Technology Harbin China Shanghai Academy of Spaceflight Technology

In this paper, a novel class of finite-time high precision satellites attitude tracking controller based on nonsingular terminal sliding mode control Lyapunov function (NTSMCLF) with the Stewart platform active vibration isolation is proposed. An active vibration algorithm for Stewart platform is designed to isolate the micro-vibration, which uses sliding mode variable Structure control (SMC) method. In order to suppress the influence of vibration on satellite, the dynamical model of Stewart platform considering the physical constraints, noise immunity and robustness of passive vibration isolation mechanical structures is designed. Considering the controller accuracy and infinite time convergence, the finite-time control Lyapunov function (CLF) high-precision attitude tracking control algorithm based on nonsingular terminal sliding mode (NTSM) is provided. The finite-time stability of the system is guaranteed. Numerical simulation results are provided to validate the effectiveness of proposed control method.

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Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against.Composite Attacks: A Two-Layered Framework

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 969-984页

作者： Xin Gong Michael V.Basin Zhiguang Feng Tingwen Huang Yukang Cui School of Cyber Science and Engineering Southeast UniversityNanjing 210096the School of AstronauticsNorthwestern Polytechnical UniversityXi’an 710072 Department of Mechanical Engineering The University of Hong KongHong Kong 999077China School of Physical and Mathematical Sciences the Autonomous University of Nuevo LeonNaevo Leon 66455Mexico Ningbo Institute of Intelligent Equipment Technology Ningbo 315299China College of Intelligent Systems Science and Engineering Harbin Engineering UniversityHarbin 150001China Texas A&M University at Qatar Doha 23874Qatar Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots the College of Mechatronics and Control EngineeringShenzhen UniversityShenzhen 518060 Peng Cheng Laboratory Shenzhen 518000China

This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against.composite attacks,including denial-of-services(DoS)attacks,false-data injection attacks,camouflage attacks,and actuation attacks(AAs).Inspired by the concept of digital twin,a new two-layered protocol equipped with a safe and private twin layer(TL)is proposed,which decouples the above problems into the defense scheme against.DoS attacks on the TL and the defense scheme against.AAs on the cyber-physical ***,a topologyrepairing strategy against.frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme,which saves communication resources *** upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is ***,a decentralized adaptive and chattering-relief controller against.potentially unbounded AAs is ***,this novel adaptive controller can achieve uniformly ultimately bounded convergence,whose error bound can be given *** practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.

关键词： Composite attacks multi-UAV systems resilient control time-varying formation-tracking

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Mn-based MXene with high lithium-ion storage capacity

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Nano Research 2024年第5期17卷 4181-4191页

作者： Yanyan Wu Dongqing Liu Xiaonan Wang Usman Ghani Muhammad Asim Mushtaq Jinfeng Yang Huarui Sun Panagiotis Tsiakaras Xingke Cai School of Science and Ministry of Industry and Information Technology Key Laboratory of Micro-Nano Optoelectronic Information System Harbin Institute of Technology ShenzhenShenzhen 5180552 China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China Institute for Advanced Study Shenzhen UniversityShenzhen 518060China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China Laboratory of Alternative Energy Conversion Systems Department of Mechanical EngineeringSchool of EngineeringUniversity of ThessalyPedion Areos 38834Greece

3d-transition metal(Fe,Co,Ni,and Mn)-based MXene materials have been predicted to demonstrate exceptional electrochemical performance because of their good electrical conductivity and the presence of metallic atoms with multiple charge ***,until now,there have been no reports on MXenes based on Fe,Co,Ni,and Mn,due to the lack of 3d-metal-layered ***,we successfully synthesized the first 3d-transition metal-based MXenes,Mn_(2)CT_(x) by exfoliating a layered precursor derived from the anti-perovskite bulk *** as-prepared Mn_(2)CT_(x) MXene nanosheets were employed as anode materials in lithium-ion batteries,which exhibited stable storage capacity of 764.7 mAh·g^(-1) at 0.5 C,placing its storage capacities at an upper-middle level compared with other reported MXene materials as well as other Mn-based anode ***,this study opens a new avenue for MXene research by synthesizing 3d-transition metal-based MXenes for electrochemical applications.

关键词： Mn_(3)GaC Mn_(2)CT_(x) MXenes lithium-ion battery anode materials negative fading capacity

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Interactive Continual Learning: Fast and Slow Thinking

Interactive Continual Learning: Fast and Slow Thinking

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Biqing Qi Xinquan Chen Junqi Gao Dong Li Jianxing Liu Ligang Wu Bowen Zhou Department of Control Science and Engineering Harbin Institute of Technology Department of Electronic Engineering Tsinghua University Frontis.AI Beijing School of Mathematics Harbin Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Advanced life forms, sustained by the synergistic interaction of neural cognitive mechanisms, continually acquire and transfer knowledge throughout their lifespan. In contrast, contemporary machine learning paradigms exhibit limitations in emulating the facets of continual learning (CL). Nonetheless, the emergence of large language models (LLMs) presents promising avenues for realizing CL via interactions with these models. Drawing on Complementary Learning System theory, this paper presents a novel Interactive Continual Learning (ICL) framework, enabled by collaborative interactions among models of various sizes. Specifically, we assign the ViT model as System1 and multimodal LLM as System2. To enable the memory module to deduce tasks from class information and enhance Set2Set retrieval, we propose the Class-Knowledge-Task Multi-Head Attention (CKT-MHA). Additionally, to improve memory retrieval in System1 through enhanced geometric representation, we introduce the CL-vMF mechanism, based on the von Mises-Fisher (vMF) distribution. Mean-while, we introduce the von Mises-Fisher Outlier Detection and Interaction (vMF-ODI) strategy to identify hard examples, thus enhancing collaboration between System1 and System2 for complex reasoning realization. Comprehensive evaluation of our proposed ICL demonstrates significant resistance to forgetting and superior performance relative to existing methods. Code is available at ***/ICL.

关键词： Continuing education Learning systems Resistance Computer vision Large language models Collaboration Memory modules

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Quadrotor control using Reinforcement Learning under Wind Disturbance

Quadrotor Control using Reinforcement Learning under Wind Di...

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第35届中国控制与决策会议

作者： Songshuo Lu Yanjie Li Zihan Liu the Department of Control Science and Engineering Harbin Institute of Technology (Shenzhen)

Quadrotors usually suffer various external disturbances when flying in the real-world environment.A robust and adaptive controller is required to maintain the *** control methods require to handcraft the model of the disturbance,making them difficult to adapt to various *** this paper,a reinforcement-learning-based(RLbased) quadrotor control method is proposed to counter external *** using Proximal Policy Optimization(PPO),a policy network is trained to directly output the desired 3-axis Euler angles and throttle for feedback *** simulated experiments and real-world experiments,we show quantitative results via the comparison between the traditional method and our proposed method.

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A Data-Driven Two-Stage Dynamometer Diagram Inference Approach for Variable Operational Conditions in Pumping Machines

A Data-Driven Two-Stage Dynamometer Diagram Inference Approa...

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Industrial Cyber-Physical Systems (ICPS)

作者： Jiusi Zhang Hao Luo Jilun Tian Xinyu Qiao Xiaoyi Xu Yuchen Jiang Shen Yin Okyay Kaynak Department of Control Science and Engineering Harbin Institute of Technology Harbin China Faculty of Engineering Norwegian University of Science and Technology Trondheim Norway Department of Electrical and Electronics Engineering Bogazici University Istanbul Turkey

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

In recent years, the soft measurement and state estimation of key parameters of industrial control systems and critical infrastructure have become crucial. The acquisition of dynamometer diagrams for pumping machines holds great significance in monitoring the operational status of drilling industrial control systems. To achieve the accurate inference of dynamometer diagrams from electrical parameters under variable operational conditions, this paper proposes a data-driven two-stage approach. Specifically, without prior knowledge, this paper proposes an electrical parameter derivation diagram approach based on extreme gradient boosting (XGBoost), which can accurately online suspended load and displacement estimation, thereby enabling efficient dynamometer diagram deduction. Furthermore, facing the variable operational conditions in actual engineering, this paper proposes a two-stage strategy. From a fine-grained perspective, the XGBoost algorithm is constructed separately for the operational condition categories and dynamometer diagram elements. This paper verifies the proposed dynamometer diagram approach under variable operational conditions through the real-world data provided by Daqing Oilfield Research inst.tute. The experimental result demonstrates the advantages of the proposed approach.

关键词： Employee welfare Knowledge engineering Industrial control Pumps Poles and towers Dynamometers Data models

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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 mainst.eam 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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Attack prevention and detection for cyber-physical systems based on coprime factorization technique

Attack prevention and detection for cyber-physical systems b...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Shimeng Wu Hao Luo Yuchen Jiang Kuan Li Department of Control Science and Engineering Harbin Institute of Technology Harbin China Shanghai Aerospace Control Technology Institute Shanghai China

While cyber-physical systems (CPSs) bring convenience to industrial production and human life, they are, in the mean time, exposed to the threat of cyber-physical attacks, which have the characteristics of concealment and vital destruction. Recent reports on several horrible attack events aroused people’s attention to the security of CPSs. Motivated by this, this paper proposes a defense scheme based on co-prime factorization technology, consisting of attack prevention and detection. This scheme utilizes residual signals that are commonly used in fault diagnosis to realize secure transmission because these residual signals do not contain essential system knowledge. At the same time, a filter module is added, which makes use of noise signals to cover and protect the reference signals and the operation status of the system. By this means, the secure transmission method reduces the leakage of system information and interferes with the design of stealthy attacks, which need sufficient system knowledge. Furthermore, it realizes the detection of non-stealthy attacks under the same framework. Simulation results on a linear system with random parameters demonstrate the effectiveness of the proposed scheme.

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A Subspace-aided Fault Diagnosis Approach for Electrical Drive Systems

A Subspace-aided Fault Diagnosis Approach for Electrical Dri...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Xinyu Qiao Hao Wang Xiaoyi Xu Hao Luo Kuan Li Department of Control Science and Engineering Harbin Institute of Technology Harbin China Shanghai Aerospace Control Technology Institute Shanghai China

For the safe operation of electrical drive systems, a data-driven fault diagnosis approach is proposed by subspace-aided signal reconstruction in this paper. Specifically, based on the subspace identification method (SIM), the frequency estimation of the signal is reformulated into the eigenvalue identification, which can be directly extracted from the time-domain signal. The subspace frequency estimation approach can avoid the problem of spectrum leakage. Under the known frequency estimation, the amplitude and phase estimation are adopted by the least squares (LS) method. The signal is reconstructed for fault detection by estimating frequency, phase, and amplitude. The fault detection scheme is designed to be helpful for the implementation of fault diagnosis in the periodic signal. Eventually, the effectiveness of the proposed approach is verified.

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