Pub Date : 2022-04-20DOI: 10.1080/21642583.2022.2063202
Dan-Ni Yang, Jingyi Lu, Hongli Dong, Zhongrui Hu
This paper considers the problem of effective feature extraction of acoustic signals from oil and gas pipelines under different working conditions. A feature extraction of pipeline leakage detection method is proposed based on multi-feature entropy fusion and local linear embedding (LLE). First, seven kinds of commonly used entropy which can reflect the characteristics of the signal better are extracted from the pipeline signal through experiments, including permutation entropy, envelope entropy, approximate entropy, fuzzy entropy, energy entropy, sample entropy and dispersion entropy. The seven-dimensional feature vectors are obtained by feature fusion. Second, the LLE algorithm is used to reduce the dimension of the feature vector to complete the secondary feature extraction. Finally, the support vector machine (SVM) is used to identify the working conditions of the pipeline. The experimental results show that, compared with other dimensionality reduction methods, single-feature entropy method and multi-feature entropy fusion method, the proposed method can identify the types of pipeline working conditions effectively and reduce the problems of false negatives and false positives in pipeline leakage detection.
{"title":"Pipeline signal feature extraction method based on multi-feature entropy fusion and local linear embedding","authors":"Dan-Ni Yang, Jingyi Lu, Hongli Dong, Zhongrui Hu","doi":"10.1080/21642583.2022.2063202","DOIUrl":"https://doi.org/10.1080/21642583.2022.2063202","url":null,"abstract":"This paper considers the problem of effective feature extraction of acoustic signals from oil and gas pipelines under different working conditions. A feature extraction of pipeline leakage detection method is proposed based on multi-feature entropy fusion and local linear embedding (LLE). First, seven kinds of commonly used entropy which can reflect the characteristics of the signal better are extracted from the pipeline signal through experiments, including permutation entropy, envelope entropy, approximate entropy, fuzzy entropy, energy entropy, sample entropy and dispersion entropy. The seven-dimensional feature vectors are obtained by feature fusion. Second, the LLE algorithm is used to reduce the dimension of the feature vector to complete the secondary feature extraction. Finally, the support vector machine (SVM) is used to identify the working conditions of the pipeline. The experimental results show that, compared with other dimensionality reduction methods, single-feature entropy method and multi-feature entropy fusion method, the proposed method can identify the types of pipeline working conditions effectively and reduce the problems of false negatives and false positives in pipeline leakage detection.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"407 - 416"},"PeriodicalIF":4.1,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48438866","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 : 2022-04-20DOI: 10.1080/21642583.2022.2052997
Yu Wang, Jiaming Zhang, Shuo Xu, Baihai Zhang
D2D communication technology has been more and more widely used as a mobile communication technology that can perform specific services in specific areas. This paper introduces D2D technology into the offshore ships' communication system and proposes a channel resource allocation scheme for interference control in the system, so as to increase the number of devices in the network. This paper first establishes the model of the offshore ships' communication system and then applies the Hungarian algorithm based on the maximization of the average position. Finally, the comparative simulation experiments of the algorithms are proposed, which could show that the Hungarian algorithm based on model application can effectively control interference, and reduce the impact after introducing D2D communication devices into the network.
{"title":"Resource allocation of offshore ships' communication system based on D2D technology","authors":"Yu Wang, Jiaming Zhang, Shuo Xu, Baihai Zhang","doi":"10.1080/21642583.2022.2052997","DOIUrl":"https://doi.org/10.1080/21642583.2022.2052997","url":null,"abstract":"D2D communication technology has been more and more widely used as a mobile communication technology that can perform specific services in specific areas. This paper introduces D2D technology into the offshore ships' communication system and proposes a channel resource allocation scheme for interference control in the system, so as to increase the number of devices in the network. This paper first establishes the model of the offshore ships' communication system and then applies the Hungarian algorithm based on the maximization of the average position. Finally, the comparative simulation experiments of the algorithms are proposed, which could show that the Hungarian algorithm based on model application can effectively control interference, and reduce the impact after introducing D2D communication devices into the network.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"200 - 207"},"PeriodicalIF":4.1,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44030648","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 : 2022-04-20DOI: 10.1080/21642583.2022.2062479
Jun Hu, Yongfeng Wang, Shuai Cheng, Jiaxin Liu, Jiawen Kang, Wenxing Yang
Object detection, which is one of the most fundamental visual recognition tasks, has been a hotspot in computer vision. CNN (Convolutional Neural Networks) have been widely employed for building detector. Due to the success of RPN (Region Proposal Network), the two-stage detectors get both classification accuracy and precise regression bounding boxes. However, they still struggle in small-size object detection. In this paper, we present a deep network, namely Spatial Fine-Grained Network (SFGN). The SFGN that exploits Spatial Fine-Grained Features (SFGF) concatenates the higher resolution features, which is fine-grained with the low resolution features and high-level semantic by stacking spatial features for fine-grained features. An enhanced region proposal generator is proposed to get the objectless for small object to obtain a small set of proposal. The contextual information surrounding the region of interest is embedded using local spatial information for increasing the useful information and discriminating the background. For improving the detection performance, we use a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region proposal generator. It embeds an efficiently implemented soft non-maximum suppression (soft-NMS) for replacing with tradition NMS to obtain consistent improvements without increasing the computational complexity in inference. On PASCAL VOC 2007 and PASCAL VOC 2012 datasets, our SFGN improves baseline model from 81.2% mAP to 80.6% mAP. On MS COCO dataset, SFGN also performs better than baseline model. As intuition suggests, our detection results provide strong evidence that our SFGN improves detection accuracy, especially in small object test.
{"title":"SFGNet detecting objects via spatial fine-grained feature and enhanced RPN with spatial context","authors":"Jun Hu, Yongfeng Wang, Shuai Cheng, Jiaxin Liu, Jiawen Kang, Wenxing Yang","doi":"10.1080/21642583.2022.2062479","DOIUrl":"https://doi.org/10.1080/21642583.2022.2062479","url":null,"abstract":"Object detection, which is one of the most fundamental visual recognition tasks, has been a hotspot in computer vision. CNN (Convolutional Neural Networks) have been widely employed for building detector. Due to the success of RPN (Region Proposal Network), the two-stage detectors get both classification accuracy and precise regression bounding boxes. However, they still struggle in small-size object detection. In this paper, we present a deep network, namely Spatial Fine-Grained Network (SFGN). The SFGN that exploits Spatial Fine-Grained Features (SFGF) concatenates the higher resolution features, which is fine-grained with the low resolution features and high-level semantic by stacking spatial features for fine-grained features. An enhanced region proposal generator is proposed to get the objectless for small object to obtain a small set of proposal. The contextual information surrounding the region of interest is embedded using local spatial information for increasing the useful information and discriminating the background. For improving the detection performance, we use a simple yet surprisingly effective online hard example mining (OHEM) algorithm for training region proposal generator. It embeds an efficiently implemented soft non-maximum suppression (soft-NMS) for replacing with tradition NMS to obtain consistent improvements without increasing the computational complexity in inference. On PASCAL VOC 2007 and PASCAL VOC 2012 datasets, our SFGN improves baseline model from 81.2% mAP to 80.6% mAP. On MS COCO dataset, SFGN also performs better than baseline model. As intuition suggests, our detection results provide strong evidence that our SFGN improves detection accuracy, especially in small object test.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"388 - 406"},"PeriodicalIF":4.1,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42686786","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 : 2022-04-18DOI: 10.1080/21642583.2022.2063203
Peng-An Wen, Xuerong Li, Nan Hou, Shujuan Mu
In this paper, we investigate the distributed recursive fault estimation problem for a class of discrete time-varying systems with binary encoding schemes over a sensor network. The fault signal with zero second-order difference is taken into account to reflect the sensor failures. Since the communication bandwidth in practice is constrained, the binary encoding schemes are exploited to regulate the signal transmission from the neighbouring sensors to the local fault estimator. In addition, due to the influence of channel noises, each bit might change with a small crossover probability. In the presence of sensor faults and bit errors, an upper bound for the estimation error covariance matrix is ensured and minimized at each time step via designing the gain matrices of the estimator. Finally, the effectiveness of the method is verified by a simulation.
{"title":"Distributed recursive fault estimation with binary encoding schemes over sensor networks","authors":"Peng-An Wen, Xuerong Li, Nan Hou, Shujuan Mu","doi":"10.1080/21642583.2022.2063203","DOIUrl":"https://doi.org/10.1080/21642583.2022.2063203","url":null,"abstract":"In this paper, we investigate the distributed recursive fault estimation problem for a class of discrete time-varying systems with binary encoding schemes over a sensor network. The fault signal with zero second-order difference is taken into account to reflect the sensor failures. Since the communication bandwidth in practice is constrained, the binary encoding schemes are exploited to regulate the signal transmission from the neighbouring sensors to the local fault estimator. In addition, due to the influence of channel noises, each bit might change with a small crossover probability. In the presence of sensor faults and bit errors, an upper bound for the estimation error covariance matrix is ensured and minimized at each time step via designing the gain matrices of the estimator. Finally, the effectiveness of the method is verified by a simulation.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"417 - 427"},"PeriodicalIF":4.1,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42309546","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 : 2022-04-04DOI: 10.1080/21642583.2022.2057371
Mingxuan Shen, Xue Gong, Yingjuan Yang
In this paper, we investigate the stability of highly nonlinear hybrid neutral pantograph stochastic differential equations (NPSDEs) with general decay rate. By applying the method of the Lyapunov function, the pth moment and almost sure stability with general decay rate of solution for NPSDEs are derived. Finally, an example is presented to show the effectiveness of the proposed methods.
{"title":"Stability of neutral pantograph stochastic differential equations with generalized decay rate","authors":"Mingxuan Shen, Xue Gong, Yingjuan Yang","doi":"10.1080/21642583.2022.2057371","DOIUrl":"https://doi.org/10.1080/21642583.2022.2057371","url":null,"abstract":"In this paper, we investigate the stability of highly nonlinear hybrid neutral pantograph stochastic differential equations (NPSDEs) with general decay rate. By applying the method of the Lyapunov function, the pth moment and almost sure stability with general decay rate of solution for NPSDEs are derived. Finally, an example is presented to show the effectiveness of the proposed methods.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"192 - 199"},"PeriodicalIF":4.1,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43634700","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 : 2022-03-31DOI: 10.1080/21642583.2022.2052995
Ying Cao, S. Ko
In materials science, an outlier may be due to variability in measurement, or it may indicate experimental errors. In this paper, we used an unsupervised method to remove outliers before further data-driven material analysis. Recently, autoencoder networks have achieved excellent results by minimizing reconstruction error. However, autoencoders do not promote the separation between outliers and inliers. The proposed SRAE model integrates latent representation to optimize the reconstruction error and ensures that outliers always deviate from the dataset in the compressed representation space. Experiments on the Nd-Fe-B magnetic materials dataset also show that after removing outliers with the proposed method, the prediction result of material property is significantly improved, indicating that the outlier detection effect is excellent.
{"title":"Fusing separated representation into an autoencoder for magnetic materials outlier detection","authors":"Ying Cao, S. Ko","doi":"10.1080/21642583.2022.2052995","DOIUrl":"https://doi.org/10.1080/21642583.2022.2052995","url":null,"abstract":"In materials science, an outlier may be due to variability in measurement, or it may indicate experimental errors. In this paper, we used an unsupervised method to remove outliers before further data-driven material analysis. Recently, autoencoder networks have achieved excellent results by minimizing reconstruction error. However, autoencoders do not promote the separation between outliers and inliers. The proposed SRAE model integrates latent representation to optimize the reconstruction error and ensures that outliers always deviate from the dataset in the compressed representation space. Experiments on the Nd-Fe-B magnetic materials dataset also show that after removing outliers with the proposed method, the prediction result of material property is significantly improved, indicating that the outlier detection effect is excellent.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"181 - 191"},"PeriodicalIF":4.1,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48323323","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 : 2022-03-15DOI: 10.1080/21642583.2022.2048322
Huimin Tao, Hailong Tan, Qiwen Chen, Hongjian Liu, Jun Hu
This study deals with the problem of the state estimation for discrete-time memristive neural networks with time-varying delays, where the output is subject to randomly occurring denial-of-service attacks. The average dwell time is used to describe the attack rules, which makes the randomly occurring denial-of-service attack more universal. The main purpose of the addressed issue is to contribute with a state estimation method, so that the dynamics of the error system is exponentially mean-square stable and satisfies a prescribed disturbance attenuation level. Sufficient conditions for the solvability of such a problem are established by employing the Lyapunov function and stochastic analysis techniques. Estimator gain is described explicitly in terms of certain linear matrix inequalities. Finally, the effectiveness of the proposed state estimation scheme is proved by a numerical example.
{"title":"H ∞ state estimation for memristive neural networks with randomly occurring DoS attacks","authors":"Huimin Tao, Hailong Tan, Qiwen Chen, Hongjian Liu, Jun Hu","doi":"10.1080/21642583.2022.2048322","DOIUrl":"https://doi.org/10.1080/21642583.2022.2048322","url":null,"abstract":"This study deals with the problem of the state estimation for discrete-time memristive neural networks with time-varying delays, where the output is subject to randomly occurring denial-of-service attacks. The average dwell time is used to describe the attack rules, which makes the randomly occurring denial-of-service attack more universal. The main purpose of the addressed issue is to contribute with a state estimation method, so that the dynamics of the error system is exponentially mean-square stable and satisfies a prescribed disturbance attenuation level. Sufficient conditions for the solvability of such a problem are established by employing the Lyapunov function and stochastic analysis techniques. Estimator gain is described explicitly in terms of certain linear matrix inequalities. Finally, the effectiveness of the proposed state estimation scheme is proved by a numerical example.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"154 - 165"},"PeriodicalIF":4.1,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45525955","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 : 2022-03-15DOI: 10.1080/21642583.2022.2048320
Nan Wang, Zhumu Fu, Fazhan Tao, Shuzhong Song, Min Ma
This paper studies fixed-time tracking problems for stochastic nonlinear systems in strict-feedback form. Different from previous results, the practical fixed-time bounded theorem for stochastic nonlinear systems is given. The unknown functions of the stochastic nonlinear systems are approximated by the Fuzzy logic system (FLS) which has a universal approximation. Then by using a back-stepping method, a novel adaptive fuzzy fixed-time controller is designed for stochastic nonlinear systems based on the fixed-time bounded theorem. The states of the stochastic nonlinear systems are guaranteed to converge into an equilibrium point contained compact set semi-globally in fixed-time by the designed controller. Finally, a numerical example and a vehicle tracking model example are provided to illustrate the proposed strategy.
{"title":"Adaptive fuzzy fixed-time control for a class of strict-feedback stochastic nonlinear systems","authors":"Nan Wang, Zhumu Fu, Fazhan Tao, Shuzhong Song, Min Ma","doi":"10.1080/21642583.2022.2048320","DOIUrl":"https://doi.org/10.1080/21642583.2022.2048320","url":null,"abstract":"This paper studies fixed-time tracking problems for stochastic nonlinear systems in strict-feedback form. Different from previous results, the practical fixed-time bounded theorem for stochastic nonlinear systems is given. The unknown functions of the stochastic nonlinear systems are approximated by the Fuzzy logic system (FLS) which has a universal approximation. Then by using a back-stepping method, a novel adaptive fuzzy fixed-time controller is designed for stochastic nonlinear systems based on the fixed-time bounded theorem. The states of the stochastic nonlinear systems are guaranteed to converge into an equilibrium point contained compact set semi-globally in fixed-time by the designed controller. Finally, a numerical example and a vehicle tracking model example are provided to illustrate the proposed strategy.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"142 - 153"},"PeriodicalIF":4.1,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45953250","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 : 2022-03-09DOI: 10.1080/21642583.2022.2048321
Xiao Jiang, Pengjiang Qian, Yizhang Jiang, Yi Gu, Aiguo Chen
Deep clustering uses neural networks to learn the low-dimensional feature representations suitable for clustering tasks. Numerous studies have shown that learning embedded features and defining the clustering loss properly contribute to better performance. However, most of the existing studies focus on the deep local features and ignore the global spatial characteristics of the original data space. To address this issue, this paper proposes deep self-supervised clustering with embedding adjacent graph features (DSSC-EAGF). The significance of our efforts is three-fold: 1) To obtain the deep representation of the potential global spatial structure, a dedicated adjacent graph matrix is designed and used to train the autoencoder in the original data space; 2) In the deep encoding feature space, the KNN algorithm is used to obtain the virtual clusters for devising a self-supervised learning loss. Then, the reconstruction loss, clustering loss, and self-supervised loss are integrated, and a novel overall loss measurement is proposed for DSSC-EAGF. 3) An inverse-Y-shaped network model is designed to well learn the features of both the local and the global structures of the original data, which greatly improves the clustering performance. The experimental studies prove the superiority of the proposed DSSC-EAGF against a few state-of-the-art deep clustering methods.
{"title":"Deep self-supervised clustering with embedding adjacent graph features","authors":"Xiao Jiang, Pengjiang Qian, Yizhang Jiang, Yi Gu, Aiguo Chen","doi":"10.1080/21642583.2022.2048321","DOIUrl":"https://doi.org/10.1080/21642583.2022.2048321","url":null,"abstract":"Deep clustering uses neural networks to learn the low-dimensional feature representations suitable for clustering tasks. Numerous studies have shown that learning embedded features and defining the clustering loss properly contribute to better performance. However, most of the existing studies focus on the deep local features and ignore the global spatial characteristics of the original data space. To address this issue, this paper proposes deep self-supervised clustering with embedding adjacent graph features (DSSC-EAGF). The significance of our efforts is three-fold: 1) To obtain the deep representation of the potential global spatial structure, a dedicated adjacent graph matrix is designed and used to train the autoencoder in the original data space; 2) In the deep encoding feature space, the KNN algorithm is used to obtain the virtual clusters for devising a self-supervised learning loss. Then, the reconstruction loss, clustering loss, and self-supervised loss are integrated, and a novel overall loss measurement is proposed for DSSC-EAGF. 3) An inverse-Y-shaped network model is designed to well learn the features of both the local and the global structures of the original data, which greatly improves the clustering performance. The experimental studies prove the superiority of the proposed DSSC-EAGF against a few state-of-the-art deep clustering methods.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"336 - 346"},"PeriodicalIF":4.1,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48785690","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 : 2022-03-06DOI: 10.1080/21642583.2022.2045644
Weiqiang Tang
A novel attitude control algorithm is developed for spacecrafts based on the model predictive control and proportional-integral observer (PIO) in the presence of constraints. The high dimensional nonlinear dynamics are firstly transformed into three single-input single-output linear structural subsystems with coupled actions. Then these actions are viewed as disturbances estimated by the PIOs, and their values are embedded into the models to improve the accuracy of prediction. The predictive controller is composed of the analytical solution and the heuristic constraint handling. In addition, the angular position information is only needed for implementation. Finally, several simulations are used to verify the effectiveness of the developed algorithm. The results show that the designed system compensates the coupled actions well and makes the attitude control performance good.
{"title":"Output feedback model predictive control of spacecrafts based on proportional-integral observer","authors":"Weiqiang Tang","doi":"10.1080/21642583.2022.2045644","DOIUrl":"https://doi.org/10.1080/21642583.2022.2045644","url":null,"abstract":"A novel attitude control algorithm is developed for spacecrafts based on the model predictive control and proportional-integral observer (PIO) in the presence of constraints. The high dimensional nonlinear dynamics are firstly transformed into three single-input single-output linear structural subsystems with coupled actions. Then these actions are viewed as disturbances estimated by the PIOs, and their values are embedded into the models to improve the accuracy of prediction. The predictive controller is composed of the analytical solution and the heuristic constraint handling. In addition, the angular position information is only needed for implementation. Finally, several simulations are used to verify the effectiveness of the developed algorithm. The results show that the designed system compensates the coupled actions well and makes the attitude control performance good.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"126 - 133"},"PeriodicalIF":4.1,"publicationDate":"2022-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46336144","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}
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