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Safe RL-based Adaptive Cooperative Game control of Wing Deformation and Flight State Tracking for Morphing Hypersonic Vehicles

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Wang, Jun-Wei Wang, Feng Wu, Huai-Ning Liu, Zhou-Yang University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Hangzhou International Innovation Institute Hangzhou311115 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

This study addresses the issue of adaptive cooperative game control (ACGC) design for wing deformation and flight state tracking of morphing hypersonic vehicles (MHVs) with variable wing shapes, input constraints and state constraints. To access a better flight control performance, the wing deformation parameter is extended in this work as an auxiliary control input. An oriented-coordinated-control nonlinear differential equation model is first established to describe the complicated longitudinal flight state tracking error dynamics of the MHV. Then, a control barrier function (CBF) is introduced and incorporated into the value function as a risk penalty term of state security constraint. Based on the differential game (DG) theory, the optimal coordinated control problem for the MHV with input constraints and state constraints is treated as a constrained cooperative DG that is further formulated as a Hamilton-Jacobi-Bellman (HJB) equation with barrier and hyperbolic functions. A safe RL algorithm is proposed to solve the above HJB equation and an online Pareto joint control strategy is achieved thereby. Hereafter the stability analysis of the closed-loop state tracking error system is done by the Lyapunov technique. Meanwhile, both state and input constraints are met during the the parameter learning regulation process. At last, a simulation study is performed to support the proposed safe RL-ACGC algorithm. © 2025 IEEE.

关键词： Hyperbolic functions

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Compound Fault Separation and Diagnosis Method Based on FSA-CNN and DAN

Compound Fault Separation and Diagnosis Method Based on FSA-...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Jiechao Dong Liping Yan Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665401166

With the industry becoming larger and more complicated, the technology of fault diagnosis becomes more and more important. Due to the strong coupling and non-stationarity of the compound fault, the existing fault diagnosis methods cannot accurately identify all single faults’ detailed information contained in the compound fault. This paper proposes a compound fault separation and diagnosis method based on FSACNN and DAN. Firstly, in order to highlight certain frequency segments, frequency segment attention module is added to CNN. Secondly, a compound fault feature separation framework based on DAN is proposed, which can separate compound fault to two fault components accurately. Thiredly, a signature matrix is introduced into ELM to improve the performance of the classifier. Finally, ablation experiments are designed to prove the advantage of the proposed method.

关键词： Fault diagnosis Industries Couplings Fault detection Feature extraction Safety Compounds

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Generating Waste Heat Energy from CPU Based on Thermoelectric Power Generator

Generating Waste Heat Energy from CPU Based on Thermoelectri...

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Chinese Automation Congress (CAC)

作者： Kun Qian Yeyun Cai Kun Xu Fang Deng School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Control and Decision of Complex Systems

ISBN: (纸本)9781665426480

Heat dissipation is extremely important for electronic component such as CPU or GPU of the computer. Now most researchers only design optimal heat sink for CPU for better heat dissipation, while few of them consider waste heat recycling of CPU. The CPU can be used as the heat source, while the original system has fan and heat sink as the cooling system. Thus, the original CPU working condition is very suitable for thermoelectric power generation and will bring great benefit to system itself. However, adding thermoelectric generator will increase the temperature of CPU, which will decrease the performance of the computer. It is necessary to reduce the hot side temperature while generating energy as much as possible. To achieve this, this paper designed a kind of intelligent waste heat generation system based on thermoelectric power generator. Three different shape heat sinks are designed, contrasted, and tested. By building experimental platform and establishing simulation model, the power of thermoelectric power generation and temperature change of the three heat sink structures are compared. The fin shaped heat sink is considered has the best output performance while keeping the hot side temperature within the acceptance range. The maximum output power is 10.67mW, and the maximum hot side temperature is 72°C during the experiment.

关键词： Temperature distribution Shape Cogeneration Generators Central Processing Unit Waste heat Trigeneration

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Unsupervised Anomaly Detection in Multivariate Time Series through Transformer-based Variational Autoencoder

Unsupervised Anomaly Detection in Multivariate Time Series t...

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第33届中国控制与决策会议

作者： Hongwei Zhang Yuanqing Xia Tijin Yan Guiyang Liu Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Alibaba Group

Modern industrial devices often use multiple sensors to detect the status of system,which produce a large amount of multivariate time *** to the complex temporal dependency of intra-channel and inter-correlations among different channels,few of proposed algorithms have addressed these challenges for anomaly detection in multivariate time ***,previous work does not consider future dependency,which has been shown to be critical for sequential data *** this paper,we develop an unsupervised anomaly detection algorithm TransAnomaly,which integrates Transformer,variational autoencoder(VAE) and nonlinear state space *** not only reduces the computational complexity and allows for more parallelization but also provides explainable *** the best of our knowledge,it is the first model that combines VAE and Transformer for multivariate time series anomaly *** experiments on several public real-world datasets show that TransAnomaly outperforms state-of-the-art baseline methods while training cost is reduced by nearly 80%.

关键词： Multivariate Time Series Anomaly Detection Transformer Parallelization

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Impulsive Formation control of Nonlinear Leader-Following Multi-agent systems with Input Saturation*

Impulsive Formation Control of Nonlinear Leader-Following Mu...

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Industrial Cyber-Physical systems (ICPS)

作者： Ni Zhang Xiaowei Jiang Xianhe Zhang Le You School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

Leader-following formation analysis problem for a second-order nonlinear multi-agent system(MAS) with input saturation is investigated in this paper. And the impulsive formation control algorithm is introduced in the designed protocol which only works at the impulse times. Owing to the real-world limited communication channels, input saturation is considered in the impulsive controller. Furthermore, based on Lyapunov stability theories, Kronecker properties, eigenvalue and so on, some sufficient conditions that guarantee the leader-following consensus of MAS are obtained. Lastly, several simulations are worked out to verify the correctness and effectiveness of the theoretical results.

关键词： Sufficient conditions Protocols Simulation Communication channels Cyber-physical systems control systems Formation control

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection

A Multi-Kernel Principal Component Analysis Method for Quali...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Lingxia Mu Biyu Lei Ding Liu School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (纸本)9781665465373

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction.

关键词： Correlation Automation Fault detection Feature extraction Eigenvalues and eigenfunctions Reliability Matrix decomposition

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Cross-Membrane Penetration to Nucleus of Adherent Cells Using Micropipettes Made by Borosilicate Glass and Quartz 17

Cross-Membrane Penetration to Nucleus of Adherent Cells Usin...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Xu, Xinyue Liu, Xiaoming Lin, Yuqing Li, Pengyun Liu, Fengyu Tang, Xiaoqing Huang, Qiang Arai, Tatsuo Beijing Institute of Technology Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems School of Mechatronical Engineering Beijing100081 China

ISBN: (纸本)9781728164151

In the biomicromanipulation field, single-cell manipulation is receiving attention due to its potential applications in many aspects of biomedicine. Cell injection, as a typical cell micromanipulation technique, is devoted to introducing foreign substances into living cells by penetrating a fine micropipette into cell membranes. Direct access to the intracellular environment, especially the nucleus, provides an ideal way to gain insight into the dynamic processes of living cells. This paper presents the comparison of controlled penetrations of two types of adherent cells and their nuclei by two types of micropipettes, made of borosilicate glass and quartz respectively. Due to the differences in the cell properties (e.g., height, adhesion strength), two corresponding penetration strategies optimizing the puncture angle and puncture height, are proposed to improve the success rate of the puncture process and the survival rate of cells after the puncture. Real nucleus puncture experiments adopting the optimal puncture strategy for each type of cells and carried out by a semi-automatic system, indicated a better puncture effect by quartz pipettes. Through this method, functional materials such as peptides and nanoparticles can be potentially introduced to perturb the cell activities and then realize its full automation. © 2020 IEEE.

关键词： Quartz

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Impact signal detection and recovery based on the novel potential well model

Impact signal detection and recovery based on the novel pote...

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第40届中国控制会议

作者： Shangbin Jiao Qing Zhang Shuang Lei Ding Xin Zhang Qing Wang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology

To further improve the output effect of the stochastic resonance system driven by the impact signal,this paper uses a novel potential well model as the nonlinear model of the *** the α stable noise environment,the detection performance of the stochastic resonance system driven by the impact signal is analyzed when the characteristic coefficient is used as the measurement *** effect of system parameters on the output effect is studied,and the detection of multiple impact signal are *** view of the distortion of the output signal waveform of the stochastic resonance system,the cause of the waveform distortion is analyzed based on the principle of particle dynamics,and the signal recovery system is *** output signal of the stochastic resonance system is recovered by using the similarity as the output system measurement *** simulation results show that the stochastic resonance system based on the novel potential well model has a better detection effect,and can effectively detect the time information of the impact *** recovery system can realize the estimation of the time-domain waveform of the impact signal under the background of strong noise.

关键词： Novel potential well model stochastic resonance impact signal detection signal recovery

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Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

关键词：

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Scene Classification Using Deep Networks Combined with Visual Attention

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Journal of Sensors 2022年第0期2022卷

作者： Shi, Jing Zhu, Hong Li, Yuxing Li, Yanghui Du, Sen School of Automation and Information Engineering Xi'An University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China

In view of the scene's complexity and diversity in scene classification, this paper makes full use of the contextual semantic relationships between the objects to describe the visual attention regions of the scenes and combines with the deep convolution neural networks, so that a scene classification model using visual attention and deep networks is constructed. Firstly, the visual attention regions in the scene image are marked by using the context-based saliency detection algorithm. Then, the original image and the visual attention region detection image are superimposed to obtain a visual attention region enhancement image. Furthermore, the deep convolution features of the original image, the visual attention region detection image, and the visual attention region enhancement image are extracted by using the deep convolution neural networks pretrained on the large-scale scene image dataset Places. Finally, the deep visual attention features are constructed by using the multilayer deep convolution features of the deep convolution networks, and a classification model is constructed. In order to verify the effectiveness of the proposed model, the experiments are carried out on four standard scene datasets LabelMe, UIUC-Sports, Scene-15, and MIT67. The results show that the proposed model improves the performance of the classification well and has good adaptability. © 2022 Jing Shi et al.

关键词： Convolution

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