Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164399
Fukang Zhao, Yadong Feng, Xinhua Wang
To solve the problem of group formation control, a leader-wingman formation control law imitating the behavior of geese flocks is designed, and a formation change strategy based on auction mechanism is proposed, which shortens the time and distance of formation change. Aiming at the problem of formation control in the turning section, a wingman tracking algorithm based on the advanced tracking point is given. The flight test shows that the algorithm can effectively solve the problem of long trajectory and tail swing of wingman in turn section.
{"title":"Research on autonomous control technology of group formation imitating the behavior of geese flock","authors":"Fukang Zhao, Yadong Feng, Xinhua Wang","doi":"10.1109/ISAS59543.2023.10164399","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164399","url":null,"abstract":"To solve the problem of group formation control, a leader-wingman formation control law imitating the behavior of geese flocks is designed, and a formation change strategy based on auction mechanism is proposed, which shortens the time and distance of formation change. Aiming at the problem of formation control in the turning section, a wingman tracking algorithm based on the advanced tracking point is given. The flight test shows that the algorithm can effectively solve the problem of long trajectory and tail swing of wingman in turn section.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114491814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164567
Mingzheng Yu, Ticao Jiao, Housheng Zhang, Lei Wang
The aim of this paper is to investigate the issue of stochastically exponential stability in positive nonlinear impulsive switching systems that involve random impulses. The random impulses means that the randomness of impulsive densities and the occurrence of the impulses is a Markov chain. The occurrence instants of impulses also satisfy average impulsive intervals. This research employs the multiple max-separable Lyapunov function method to investigate two cases: asynchronous/synchronous switching and impulses. Then the stochastic exponential stability conditions are given. Finally, to demonstrate the validity of the theoretical results, two illustrative examples are provided.
{"title":"Stability Analysis for Nonlinear Switched Positive Systems With Markov Chain Impulses","authors":"Mingzheng Yu, Ticao Jiao, Housheng Zhang, Lei Wang","doi":"10.1109/ISAS59543.2023.10164567","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164567","url":null,"abstract":"The aim of this paper is to investigate the issue of stochastically exponential stability in positive nonlinear impulsive switching systems that involve random impulses. The random impulses means that the randomness of impulsive densities and the occurrence of the impulses is a Markov chain. The occurrence instants of impulses also satisfy average impulsive intervals. This research employs the multiple max-separable Lyapunov function method to investigate two cases: asynchronous/synchronous switching and impulses. Then the stochastic exponential stability conditions are given. Finally, to demonstrate the validity of the theoretical results, two illustrative examples are provided.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116595687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164474
Yefan Gan, N. Lu, Baoli Zhang, Jianfei Chen, Ling Sun, Yanling Ji
As a key mechanical component in the door system of rail vehicles, the rolling pin is closely related to the safe operation of the door system. For the purpose of maintaining the safety of the door system of rail vehicles, it is necessary to accurately predict the Remaining Useful Life (RUL) of the rolling pin. Since the degree of wear is difficult to measure, it is quite hard to predict its life in real time. Synchronously, the amount of data that can characterize the life of the rolling pin is rarely available. To predict the RUL of rolling pin online as well as provide decision support for active maintenance, this paper proposes an RUL prediction method of rolling pin based on the Convolutional Neural Network (CNN) and Bi-directional Gated Recursive Unit (BiGRU), which combines the feature extraction ability of CNN and the information retention ability of BiGRU, enabling this model to be effective in dealing with several small sample issues. The simulation results demonstrate that such a method can accurately predict the life of the rolling pin, which has essential engineering application value.
{"title":"A CNN-BiGRU Based Life Prediction Method for Rolling Pins of Rail Vehicle Door System","authors":"Yefan Gan, N. Lu, Baoli Zhang, Jianfei Chen, Ling Sun, Yanling Ji","doi":"10.1109/ISAS59543.2023.10164474","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164474","url":null,"abstract":"As a key mechanical component in the door system of rail vehicles, the rolling pin is closely related to the safe operation of the door system. For the purpose of maintaining the safety of the door system of rail vehicles, it is necessary to accurately predict the Remaining Useful Life (RUL) of the rolling pin. Since the degree of wear is difficult to measure, it is quite hard to predict its life in real time. Synchronously, the amount of data that can characterize the life of the rolling pin is rarely available. To predict the RUL of rolling pin online as well as provide decision support for active maintenance, this paper proposes an RUL prediction method of rolling pin based on the Convolutional Neural Network (CNN) and Bi-directional Gated Recursive Unit (BiGRU), which combines the feature extraction ability of CNN and the information retention ability of BiGRU, enabling this model to be effective in dealing with several small sample issues. The simulation results demonstrate that such a method can accurately predict the life of the rolling pin, which has essential engineering application value.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121867483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164390
Yuran Li, X. Zhai, Ran Wang, Jing Zhu, Chenghua Yao
The combination of cognitive radio networks and unmanned aerial vehicles (UAVs) can overcome the scarcity of spectrum for wireless networks and provide important benefits for large-scale deployment of UAVs. However, energy constraint is an inevitable challenge for UAV communication. In order to prolong the lifetime of UAVs, we apply the accumulating priority queue (APQ) discipline for the first time to the queuing charging process of UAVs in cognitive radio networks. The APQ is based on the classic priority queue with the accumulation rate added to it, which means that the priority of a user is a function of the waiting time. By analyzing the values for different arrival rates and priorities, we discuss the factors that affect the average waiting times and key performance indicators. We also consider the related optimization problem and propose an improved bisection algorithm. According to the requirements of different classes of users, we can adjust the accumulation rates and arrival rates to meet the performance indicators.
{"title":"Accumulating Priority Queue for Charging of Unmanned Aerial Vehicles in Cognitive Radio Networks","authors":"Yuran Li, X. Zhai, Ran Wang, Jing Zhu, Chenghua Yao","doi":"10.1109/ISAS59543.2023.10164390","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164390","url":null,"abstract":"The combination of cognitive radio networks and unmanned aerial vehicles (UAVs) can overcome the scarcity of spectrum for wireless networks and provide important benefits for large-scale deployment of UAVs. However, energy constraint is an inevitable challenge for UAV communication. In order to prolong the lifetime of UAVs, we apply the accumulating priority queue (APQ) discipline for the first time to the queuing charging process of UAVs in cognitive radio networks. The APQ is based on the classic priority queue with the accumulation rate added to it, which means that the priority of a user is a function of the waiting time. By analyzing the values for different arrival rates and priorities, we discuss the factors that affect the average waiting times and key performance indicators. We also consider the related optimization problem and propose an improved bisection algorithm. According to the requirements of different classes of users, we can adjust the accumulation rates and arrival rates to meet the performance indicators.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129391541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164523
Rendi Liu, Ju Jiang, Xiang Liu, Haowei Sun, Tingyu Ma
In this paper, a pitching control method based on Deep Deterministic Policy Gradient (DDPG) algorithm for carrier aircraft landing and descending stage is studied. DDPG controller takes pitch angle rate error, pitch angle error and altitude error as input, and output as elevator deflection, realizing the rapid pitch angle response of carrier-aircraft under different landing states. Compared with traditional PID controller, network training of Actor-Critic for DDPG attitude controller greatly improves the calculation efficiency of control quantity, and reduces the difficulty of parameter optimization. The simulation experiment in this paper was based on the F/A-18 aircraft aerodynamics model constructed in Matlab/Simulink, and the intensive learning and training environment built on PyCharm platform was used to realize the data interaction between the two platforms through UDP communication. The simulation results show that the attitude controller based on reinforcement learning designed in this paper has the characteristics of fast response speed and small dynamic error, and meets the control accuracy requirements in the experiment.
{"title":"Carrier Aircraft Landing Control Technology Based on Deep Reinforcement Learning","authors":"Rendi Liu, Ju Jiang, Xiang Liu, Haowei Sun, Tingyu Ma","doi":"10.1109/ISAS59543.2023.10164523","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164523","url":null,"abstract":"In this paper, a pitching control method based on Deep Deterministic Policy Gradient (DDPG) algorithm for carrier aircraft landing and descending stage is studied. DDPG controller takes pitch angle rate error, pitch angle error and altitude error as input, and output as elevator deflection, realizing the rapid pitch angle response of carrier-aircraft under different landing states. Compared with traditional PID controller, network training of Actor-Critic for DDPG attitude controller greatly improves the calculation efficiency of control quantity, and reduces the difficulty of parameter optimization. The simulation experiment in this paper was based on the F/A-18 aircraft aerodynamics model constructed in Matlab/Simulink, and the intensive learning and training environment built on PyCharm platform was used to realize the data interaction between the two platforms through UDP communication. The simulation results show that the attitude controller based on reinforcement learning designed in this paper has the characteristics of fast response speed and small dynamic error, and meets the control accuracy requirements in the experiment.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124661906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164329
Chaoran Wang, Hai Zhu, Xiaozhou Zhu, C. Wu, Wen Yao, Xiaoqian Chen
This paper proposes a task scheduling framework for autonomous navigation of micro aerial vehicles (MAVs) in unknown environments. Currently, the dominant approach for task scheduling in MAV systems typically relies on the finite state machine (FSM), which presents limitations in scaling the system functionality due to the coupled relationship between modules. We propose a generic task scheduling framework for MAVs based on behavior trees (BTs) to address this challenge. A blackboard is built as a state data management center, decoupling different modules of the MAV so that the various functional modules can operate independently. In addition, we set up standardized interfaces for the modules, thus the behavior tree can quickly connect the modules. This framework enables MAVs to perform autonomous flight scheduling tasks and supports the rapid expansion of system functions. Finally, we validated the effectiveness of the framework with real-world experiments on a customized MAV.
{"title":"Task Management for Autonomous Flights of Micro Aerial Vehicles: A Behavior Tree Approach","authors":"Chaoran Wang, Hai Zhu, Xiaozhou Zhu, C. Wu, Wen Yao, Xiaoqian Chen","doi":"10.1109/ISAS59543.2023.10164329","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164329","url":null,"abstract":"This paper proposes a task scheduling framework for autonomous navigation of micro aerial vehicles (MAVs) in unknown environments. Currently, the dominant approach for task scheduling in MAV systems typically relies on the finite state machine (FSM), which presents limitations in scaling the system functionality due to the coupled relationship between modules. We propose a generic task scheduling framework for MAVs based on behavior trees (BTs) to address this challenge. A blackboard is built as a state data management center, decoupling different modules of the MAV so that the various functional modules can operate independently. In addition, we set up standardized interfaces for the modules, thus the behavior tree can quickly connect the modules. This framework enables MAVs to perform autonomous flight scheduling tasks and supports the rapid expansion of system functions. Finally, we validated the effectiveness of the framework with real-world experiments on a customized MAV.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"273 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120887491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164618
Bowen Hou, Han Yuan, Xuanying Zhou, Runran Deng, E. Wei, Ping Liu
In the vision-based navigation system, feature-based navigation method for autonomous orbit determination is a newly proposed method. Considering the camera periodic time-varying error caused by the complex on-board environment, a camera calibration method is proposed to compensate for the image point bias and the focal length variation bias combined with augmented unscented Kalman filter. The method can effectively modify the measurements without any other additional equipment except the space non-cooperative target features which extracted from the image shot by the camera. Simulation results indicate that the method can effectively modify the camera measurements and realize autonomous navigation with a higher accuracy compared with no calibration.
{"title":"Camera Calibration Method for Autonomous Navigation based on Space Non-cooperative Target Features","authors":"Bowen Hou, Han Yuan, Xuanying Zhou, Runran Deng, E. Wei, Ping Liu","doi":"10.1109/ISAS59543.2023.10164618","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164618","url":null,"abstract":"In the vision-based navigation system, feature-based navigation method for autonomous orbit determination is a newly proposed method. Considering the camera periodic time-varying error caused by the complex on-board environment, a camera calibration method is proposed to compensate for the image point bias and the focal length variation bias combined with augmented unscented Kalman filter. The method can effectively modify the measurements without any other additional equipment except the space non-cooperative target features which extracted from the image shot by the camera. Simulation results indicate that the method can effectively modify the camera measurements and realize autonomous navigation with a higher accuracy compared with no calibration.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126550287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164341
Chunlei Shi, D. Niu, Hao Gong, Mei Zhang, Zhan Cao, Yulong Jin
This paper proposes a lightweight person re-identification network that incorporates a progressive attention mechanism The network aims to address the low accuracy issue in person re-identification caused by various factors such as different viewing angles, poses, illumination conditions, occlusions, and low image resolutions. Additionally, the network design takes into consideration the need for lightweight model deployment in practical scenarios. To improve the network’s performance using limited training data, data augmentation techniques are employed to expand the training dataset and enhance the robustness of the network model. The Resnet-50 architecture serves as the backbone network, and a feature shunt structure with depthwise separable convolutions is introduced to reduce computational parameters and accelerate person retrieval inference speed. Furthermore, the feature extraction and embedding processes are separated, and a progressive attention module is introduced. This module gradually segments the features into local blocks of different granularity, allowing for the learning of discriminative features at each granularity level. This progressive approach enhances the network’s ability to perceive foreground information from coarse to fine levels and improves feature matching capability. To supervise the model, a triplet loss function is utilized, specifically designed to address challenging samples. This loss function helps reduce intra-class variations while increasing inter-class separability. The efficacy of the proposed method in person re-identification is substantiated by conducting experimental evaluation on both the Market-1501 and DukeMTMC-ReID datasets. The experimental results demonstrate that the method achieves mAP indices of 88.1% and 79.1% on the respective datasets, providing strong evidence for its effectiveness in addressing the challenges of person re-identification.
{"title":"Person Re-identification Lightweight Network Based on Progressive Attention Mechanism","authors":"Chunlei Shi, D. Niu, Hao Gong, Mei Zhang, Zhan Cao, Yulong Jin","doi":"10.1109/ISAS59543.2023.10164341","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164341","url":null,"abstract":"This paper proposes a lightweight person re-identification network that incorporates a progressive attention mechanism The network aims to address the low accuracy issue in person re-identification caused by various factors such as different viewing angles, poses, illumination conditions, occlusions, and low image resolutions. Additionally, the network design takes into consideration the need for lightweight model deployment in practical scenarios. To improve the network’s performance using limited training data, data augmentation techniques are employed to expand the training dataset and enhance the robustness of the network model. The Resnet-50 architecture serves as the backbone network, and a feature shunt structure with depthwise separable convolutions is introduced to reduce computational parameters and accelerate person retrieval inference speed. Furthermore, the feature extraction and embedding processes are separated, and a progressive attention module is introduced. This module gradually segments the features into local blocks of different granularity, allowing for the learning of discriminative features at each granularity level. This progressive approach enhances the network’s ability to perceive foreground information from coarse to fine levels and improves feature matching capability. To supervise the model, a triplet loss function is utilized, specifically designed to address challenging samples. This loss function helps reduce intra-class variations while increasing inter-class separability. The efficacy of the proposed method in person re-identification is substantiated by conducting experimental evaluation on both the Market-1501 and DukeMTMC-ReID datasets. The experimental results demonstrate that the method achieves mAP indices of 88.1% and 79.1% on the respective datasets, providing strong evidence for its effectiveness in addressing the challenges of person re-identification.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126946214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, indoor inertial navigation technology based on pedestrian dead reckoning (PDR) has been widely promoted. Traditional methods often use auxiliary facilities or environmental constraints to suppress PDR heading cumulative errors, but these auxiliary means restrict the application scope of PDR. PDR based on deep learning fills the need for external information dependence, but the heading estimation accuracy is low and the adaptability is poor. To address this problem, an optimized adaptive multitask loss layer based on uncertain weighting is proposed, which constrains the weight of position and attitude estimation in the overall prediction task and dynamically adjusts it adaptively in different stages to enhance attitude estimation capability. A PDR algorithm based on an end-to-end joint residual neural network and bidirectional long short-term memory network is designed to improve the algorithm’s generalization ability. The original inertial navigation data is processed by segmentation and coordinate normalization and is used as input to the deep learning model to detect features and predict trajectories, achieving accurate indoor pedestrian inertial navigation. Finally, the navigation performance of the proposed algorithm is validated in experiments of walking, running, and mixed gait patterns. The results show that the positioning accuracy of the proposed algorithm is better than that of traditional PDR methods and the RONIN algorithm based on deep learning. The positioning errors in walking, running, and mixed gait patterns are reduced by 21.07%, 10.34%, and 32.15%, respectively, compared to the RONIN algorithm.
{"title":"Deep Learning Pedestrian Navigation Method Based on Multi-task Loss Function*","authors":"Tao Wang, Jizhou Lai, Cheng Yuan, Jingyi Zhu, Qianqian Zhu, Pin Lyu","doi":"10.1109/ISAS59543.2023.10164517","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164517","url":null,"abstract":"In recent years, indoor inertial navigation technology based on pedestrian dead reckoning (PDR) has been widely promoted. Traditional methods often use auxiliary facilities or environmental constraints to suppress PDR heading cumulative errors, but these auxiliary means restrict the application scope of PDR. PDR based on deep learning fills the need for external information dependence, but the heading estimation accuracy is low and the adaptability is poor. To address this problem, an optimized adaptive multitask loss layer based on uncertain weighting is proposed, which constrains the weight of position and attitude estimation in the overall prediction task and dynamically adjusts it adaptively in different stages to enhance attitude estimation capability. A PDR algorithm based on an end-to-end joint residual neural network and bidirectional long short-term memory network is designed to improve the algorithm’s generalization ability. The original inertial navigation data is processed by segmentation and coordinate normalization and is used as input to the deep learning model to detect features and predict trajectories, achieving accurate indoor pedestrian inertial navigation. Finally, the navigation performance of the proposed algorithm is validated in experiments of walking, running, and mixed gait patterns. The results show that the positioning accuracy of the proposed algorithm is better than that of traditional PDR methods and the RONIN algorithm based on deep learning. The positioning errors in walking, running, and mixed gait patterns are reduced by 21.07%, 10.34%, and 32.15%, respectively, compared to the RONIN algorithm.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126431932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1109/ISAS59543.2023.10164606
Rong Zhao, Lu Liu
This paper investigates the problem of $mathcal{H}{_}{-} / mathcal{H}{_}{infty}$ fault detection (FD) filter design for continuous-time polytopic uncertain linear systems in the finite-frequency (FF) domain. By assuming that both disturbances and faults are restricted to FF ranges, we are interested in designing an FD filter such that the resulting filtering error system (FES) is both sensitive to faults and robust against disturbances. By using the generalized Kalman-Yakubovič-Popov (KYP) lemma, Projection lemma, and some elegant convexification procedures, sufficient conditions for synthesis of the FD filter are established by solving an optimization problem in the form of linear matrix inequalities (LMIs). Finally, simulation studies are provided to validate the effectiveness of the proposed filtering approach.
{"title":"Fault Detection Filter Design for Polytopic Uncertain Systems in Finite-Frequency Domain","authors":"Rong Zhao, Lu Liu","doi":"10.1109/ISAS59543.2023.10164606","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164606","url":null,"abstract":"This paper investigates the problem of $mathcal{H}{_}{-} / mathcal{H}{_}{infty}$ fault detection (FD) filter design for continuous-time polytopic uncertain linear systems in the finite-frequency (FF) domain. By assuming that both disturbances and faults are restricted to FF ranges, we are interested in designing an FD filter such that the resulting filtering error system (FES) is both sensitive to faults and robust against disturbances. By using the generalized Kalman-Yakubovič-Popov (KYP) lemma, Projection lemma, and some elegant convexification procedures, sufficient conditions for synthesis of the FD filter are established by solving an optimization problem in the form of linear matrix inequalities (LMIs). Finally, simulation studies are provided to validate the effectiveness of the proposed filtering approach.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127321121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：