Pub Date : 2024-03-29DOI: 10.1109/JMASS.2024.3406783
Bin Gao;Anna Song;Hanwen Xu;Zenan Zhang;Wenhui Lian;Lei Yang
Low-rank matrix decomposition is effective for sparse recovery. However, the conventions are limited in accuracy for high-resolution synthetic aperture radar (SAR) imagery due to the shrinkage effect in the cost function, which leads to biased estimates. To this end, an enhanced-low rank matrix decomposition (E-LRMD) SAR imaging algorithm is proposed, which employs a factor group-sparse regularization (FGSR) to approximate the intended cost function, so that the low-rank features can be represented. Since, the constructed regularization function is factorized, the singular value decomposition is avoided, and the computational burden can be reduced accordingly. Furthermore, $ell _{1}$ -norm is incorporated to encode the sparse feature. To incorporate with the enhancement of multiple features, the alternating direction method of multipliers (ADMM) framework is utilized. Therefore, both the low-rank and sparse features can be accurately recovered and enhanced, cooperatively, where the error propagation between the enhancement of multiple features is minimized. In the experiments, the effectiveness and robustness of the algorithm are verified by the simulated data and practical UAV-SAR data, respectively. Also, a phase transition diagram (PTD) experiment is carried out to analyse the advantages of the proposed algorithm in terms of quantitative aspects compared with the conventional methods.
{"title":"Enhanced Low-Rank Matrix Decomposition for High-Resolution UAV-SAR Imagery","authors":"Bin Gao;Anna Song;Hanwen Xu;Zenan Zhang;Wenhui Lian;Lei Yang","doi":"10.1109/JMASS.2024.3406783","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3406783","url":null,"abstract":"Low-rank matrix decomposition is effective for sparse recovery. However, the conventions are limited in accuracy for high-resolution synthetic aperture radar (SAR) imagery due to the shrinkage effect in the cost function, which leads to biased estimates. To this end, an enhanced-low rank matrix decomposition (E-LRMD) SAR imaging algorithm is proposed, which employs a factor group-sparse regularization (FGSR) to approximate the intended cost function, so that the low-rank features can be represented. Since, the constructed regularization function is factorized, the singular value decomposition is avoided, and the computational burden can be reduced accordingly. Furthermore, \u0000<inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>\u0000-norm is incorporated to encode the sparse feature. To incorporate with the enhancement of multiple features, the alternating direction method of multipliers (ADMM) framework is utilized. Therefore, both the low-rank and sparse features can be accurately recovered and enhanced, cooperatively, where the error propagation between the enhancement of multiple features is minimized. In the experiments, the effectiveness and robustness of the algorithm are verified by the simulated data and practical UAV-SAR data, respectively. Also, a phase transition diagram (PTD) experiment is carried out to analyse the advantages of the proposed algorithm in terms of quantitative aspects compared with the conventional methods.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"187-199"},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041442","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}
Terahertz (THz) radar imaging has been getting a lot more attention in recent years because it has a faster frame rate and better resolution. However, nonlinear phase errors resulting from the immaturity and instability of THz devices inevitably affect the transmitted signal of THz radar imaging systems, causing the range image to blur. In this work, we present an adaptive correction approach for improving the imaging quality of THz radar by the elimination of nonlinear phase error. First, the nonparametric model is created with high accuracy; this model accounts for nonlinear phase errors introduced by the signal source and other broadband hardware devices like the frequency multiplier. After that, the suggested technique employs nonlinear phase error estimates and compensation by iterative optimization, with the picture contrast of multiple pulse compression serving as the evaluation criterion. The proposed method has been validated through the use of both synthetic data and field data gathered with a 0.22-THz airborne synthetic aperture radar equipment. The experimental results further highlight the suggested method’s high robustness, low computational cost, and several potential uses.
{"title":"An Adaptive Nonlinear Phase Error Estimation and Compensation Method for Terahertz Radar Imaging System","authors":"Mengyang Zhan;Jiawei Wu;Yinwei Li;Gang Xu;Yiming Zhu","doi":"10.1109/JMASS.2024.3382942","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3382942","url":null,"abstract":"Terahertz (THz) radar imaging has been getting a lot more attention in recent years because it has a faster frame rate and better resolution. However, nonlinear phase errors resulting from the immaturity and instability of THz devices inevitably affect the transmitted signal of THz radar imaging systems, causing the range image to blur. In this work, we present an adaptive correction approach for improving the imaging quality of THz radar by the elimination of nonlinear phase error. First, the nonparametric model is created with high accuracy; this model accounts for nonlinear phase errors introduced by the signal source and other broadband hardware devices like the frequency multiplier. After that, the suggested technique employs nonlinear phase error estimates and compensation by iterative optimization, with the picture contrast of multiple pulse compression serving as the evaluation criterion. The proposed method has been validated through the use of both synthetic data and field data gathered with a 0.22-THz airborne synthetic aperture radar equipment. The experimental results further highlight the suggested method’s high robustness, low computational cost, and several potential uses.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 2","pages":"108-116"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091213","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}
Ground subsidence is a representative geohazard in mining areas that threatens human safety and infrastructure. Interferometric synthetic aperture radar (InSAR) was used to measure ground subsidence related to mining activities. However, mining areas are often subjected to severe temporal and geometric decorrelation problems, resulting in sparse persistent scatterers (PSs) and lower measurement accuracy. To improve deformation measurements, a robust multitemporal InSAR (MT-InSAR) method that jointly detects PS and distributed scatterers (DSs) in a two-tier network was utilized here. To solve the mismatch in the traditional linear velocity model, a logistic model was introduced for MT-InSAR processing. Forty-four Sentinel-1A SAR images acquired between 1 January 2020 and 30 June 2021 were used to measure ground subsidence in Zhoutaizi Village, Chengde City, Hebei Province, China, which endured geohazards induced and exacerbated by mining activities. We observed that more measurement points were produced using the logistic model (11 607) compared with the constant velocity model (10 980) in the mining areas with an increase of 5.7%, while the mean value of the standard deviation of the estimated residuals reduced from 1.45 to 1.13 with a decrease of 22%. Results are beneficial for geohazard assessment and management in mining areas.
{"title":"Mining-Related Subsidence Measurements Using a Robust Multitemporal InSAR Method and Logistic Model","authors":"Peifeng Ma;Chang Yu;Zherong Wu;Zhanze Wang;Jiehong Chen","doi":"10.1109/JMASS.2024.3381788","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3381788","url":null,"abstract":"Ground subsidence is a representative geohazard in mining areas that threatens human safety and infrastructure. Interferometric synthetic aperture radar (InSAR) was used to measure ground subsidence related to mining activities. However, mining areas are often subjected to severe temporal and geometric decorrelation problems, resulting in sparse persistent scatterers (PSs) and lower measurement accuracy. To improve deformation measurements, a robust multitemporal InSAR (MT-InSAR) method that jointly detects PS and distributed scatterers (DSs) in a two-tier network was utilized here. To solve the mismatch in the traditional linear velocity model, a logistic model was introduced for MT-InSAR processing. Forty-four Sentinel-1A SAR images acquired between 1 January 2020 and 30 June 2021 were used to measure ground subsidence in Zhoutaizi Village, Chengde City, Hebei Province, China, which endured geohazards induced and exacerbated by mining activities. We observed that more measurement points were produced using the logistic model (11 607) compared with the constant velocity model (10 980) in the mining areas with an increase of 5.7%, while the mean value of the standard deviation of the estimated residuals reduced from 1.45 to 1.13 with a decrease of 22%. Results are beneficial for geohazard assessment and management in mining areas.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"149-155"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041370","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 : 2024-03-26DOI: 10.1109/JMASS.2024.3381974
Rui Guo;Xiaopeng Zhao;Liang Guo;Ruiqi Xu;Yi Liang
In recent years, complex-valued convolutional neural networks (CNNs) have emerged as a promising approach for polarimetric synthetic aperture radar (PolSAR) image segmentation by utilizing both amplitude and phase information in PolSAR data. This article introduces a complex-valued network for PolSAR image segmentation termed as complex-valued Lovász-softmax loss optimization synthetic aperture radar network (CV-LoSARNet), which is in fact a complex-valued Lovász-softmax loss optimization framework. The bilateral structure of CV-LoSARNet provides efficient feature extraction, while the complex-valued network adapting to PolSAR data can improve feature extraction capabilities. The introduced loss function combines both the Lovász-softmax loss and cross-entropy loss, which can improve the optimization objective of the segmentation. Comparative experiments conducted on E-SAR data and AIRSAR data demonstrate the superiority of the proposed network over the classical full CNN and the classic bilateral networks. Compared with the classic bilateral network, the CV-LoSARNet has improved the mean intersection over union and mean pixel accuracy of E-SAR data sets by 2.37% and 2.29%, for AIRSAR data sets, the improvement is 12.95% and 6.70%. Moreover, the segmentation performance of the proposed network on different polarimetric modes is discussed.
{"title":"A Complex-Valued PolSAR Image Segmentation Network With Lovász-Softmax Loss Optimization","authors":"Rui Guo;Xiaopeng Zhao;Liang Guo;Ruiqi Xu;Yi Liang","doi":"10.1109/JMASS.2024.3381974","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3381974","url":null,"abstract":"In recent years, complex-valued convolutional neural networks (CNNs) have emerged as a promising approach for polarimetric synthetic aperture radar (PolSAR) image segmentation by utilizing both amplitude and phase information in PolSAR data. This article introduces a complex-valued network for PolSAR image segmentation termed as complex-valued Lovász-softmax loss optimization synthetic aperture radar network (CV-LoSARNet), which is in fact a complex-valued Lovász-softmax loss optimization framework. The bilateral structure of CV-LoSARNet provides efficient feature extraction, while the complex-valued network adapting to PolSAR data can improve feature extraction capabilities. The introduced loss function combines both the Lovász-softmax loss and cross-entropy loss, which can improve the optimization objective of the segmentation. Comparative experiments conducted on E-SAR data and AIRSAR data demonstrate the superiority of the proposed network over the classical full CNN and the classic bilateral networks. Compared with the classic bilateral network, the CV-LoSARNet has improved the mean intersection over union and mean pixel accuracy of E-SAR data sets by 2.37% and 2.29%, for AIRSAR data sets, the improvement is 12.95% and 6.70%. Moreover, the segmentation performance of the proposed network on different polarimetric modes is discussed.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 2","pages":"100-107"},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091153","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 : 2024-03-23DOI: 10.1109/JMASS.2024.3397068
{"title":"IEEE Journal on Miniaturization for Air and Space Systems Special Issue on Network Intelligence for Unmanned Aerial Vehicles","authors":"","doi":"10.1109/JMASS.2024.3397068","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3397068","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 2","pages":"125-126"},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-23DOI: 10.1109/JMASS.2024.3397026
{"title":"The Journal of Miniaturized Air and Space Systems","authors":"","doi":"10.1109/JMASS.2024.3397026","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3397026","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 2","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1109/JMASS.2024.3402984
Mariana Londoño Orozco;Belarmino Segura Giraldo
Small satellites are becoming a significant part of the space industry and educational field. Small satellite development has increased significantly during the past decades due to their low-cost development and construction facility. One of the essential parts of a satellite is the attitude determination and control system (ADCS) which dictates and controls the orientation of the satellite in space and makes the control maneuver. Still, it is also one of the systems that present more issues and that can cause a mission failure. For developing an ADCS, simulation and testing are important before implementation. This article reviews the approaches for small satellite dynamics, types of control that can be implemented in small satellites, and the devices that can be used in the ADCS, mentioning the advantages and disadvantages. Explanations about classical and modern control algorithms that are currently used for small satellites are presented to show the latest advances in the field.
{"title":"Attitude Determination and Control in Small Satellites: A Review","authors":"Mariana Londoño Orozco;Belarmino Segura Giraldo","doi":"10.1109/JMASS.2024.3402984","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3402984","url":null,"abstract":"Small satellites are becoming a significant part of the space industry and educational field. Small satellite development has increased significantly during the past decades due to their low-cost development and construction facility. One of the essential parts of a satellite is the attitude determination and control system (ADCS) which dictates and controls the orientation of the satellite in space and makes the control maneuver. Still, it is also one of the systems that present more issues and that can cause a mission failure. For developing an ADCS, simulation and testing are important before implementation. This article reviews the approaches for small satellite dynamics, types of control that can be implemented in small satellites, and the devices that can be used in the ADCS, mentioning the advantages and disadvantages. Explanations about classical and modern control algorithms that are currently used for small satellites are presented to show the latest advances in the field.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"182-186"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041371","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 : 2024-03-18DOI: 10.1109/JMASS.2024.3378726
Jialong Jian;Yong Chen;Qiuni Li;Hongbo Li;Xiaokang Zheng;Chongchong Han
Multi-UAV cooperative air combat has attracted wide attention from relative scholars. However, the decision-making problem of UAV swarm confrontation under uncertain conditions makes it more difficult. In this article, a two-layer decision-making method, containing dynamic target assignment and distributed Monte Carlo tree search (MCTS), is proposed to address this issue. Additionally, the possibility degree function method of interval gray number is combined with a genetic algorithm to deal with uncertain information in an air combat environment. Specifically, considering the actual air combat scene, the target value factor is introduced in the target allocation process, and the dynamic target allocation mechanism is established to adjust the cluster combat strategy in real time. The experiments show that the proposed two-level decision-making method can effectively deal with the swarm air combat problem under uncertain environments. First, the improved genetic algorithm can solve the problem of target allocation in an uncertain environment and give the target allocation scheme in the current state. Moreover, the establishment of the dynamic target allocation mechanism makes the cooperative behavior of UAVs emerge in the swarm, which fully reflects the adversarial air combat.
{"title":"Decision-Making Method of Multi-UAV Cooperate Air Combat Under Uncertain Environment","authors":"Jialong Jian;Yong Chen;Qiuni Li;Hongbo Li;Xiaokang Zheng;Chongchong Han","doi":"10.1109/JMASS.2024.3378726","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3378726","url":null,"abstract":"Multi-UAV cooperative air combat has attracted wide attention from relative scholars. However, the decision-making problem of UAV swarm confrontation under uncertain conditions makes it more difficult. In this article, a two-layer decision-making method, containing dynamic target assignment and distributed Monte Carlo tree search (MCTS), is proposed to address this issue. Additionally, the possibility degree function method of interval gray number is combined with a genetic algorithm to deal with uncertain information in an air combat environment. Specifically, considering the actual air combat scene, the target value factor is introduced in the target allocation process, and the dynamic target allocation mechanism is established to adjust the cluster combat strategy in real time. The experiments show that the proposed two-level decision-making method can effectively deal with the swarm air combat problem under uncertain environments. First, the improved genetic algorithm can solve the problem of target allocation in an uncertain environment and give the target allocation scheme in the current state. Moreover, the establishment of the dynamic target allocation mechanism makes the cooperative behavior of UAVs emerge in the swarm, which fully reflects the adversarial air combat.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"138-148"},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041440","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}
Feature-based target detection methods are predominantly used to determine the presence or absence of detection targets under sea clutter conditions, but they exhibit a deficiency in making nuanced classification judgments for the different categories of detected targets. Both ship targets and marine surface floating small objects display specific sea clutter characteristics to various degrees. Given the periodic consistency of transient power and the Doppler centroid bandwidth observed in sea clutter, this article examines the manifestation level of this characteristic in both categories of targets, drawing on their respective motion mechanisms. The ability to distinguish between these two categories of targets using this characteristic has been validated through the analysis of empirical data, subsequently leading to the formulation of discriminant statistics that facilitate target classification. The data confirm the effectiveness of this approach, illustrating its robust classification performance.
{"title":"A Classification Method for Marine Surface Floating Small Targets and Ship Targets","authors":"Hengli Yu;Zheng Cao;Guoqing Wang;Hao Ding;Ningbo Liu;Yunlong Dong","doi":"10.1109/JMASS.2024.3372116","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3372116","url":null,"abstract":"Feature-based target detection methods are predominantly used to determine the presence or absence of detection targets under sea clutter conditions, but they exhibit a deficiency in making nuanced classification judgments for the different categories of detected targets. Both ship targets and marine surface floating small objects display specific sea clutter characteristics to various degrees. Given the periodic consistency of transient power and the Doppler centroid bandwidth observed in sea clutter, this article examines the manifestation level of this characteristic in both categories of targets, drawing on their respective motion mechanisms. The ability to distinguish between these two categories of targets using this characteristic has been validated through the analysis of empirical data, subsequently leading to the formulation of discriminant statistics that facilitate target classification. The data confirm the effectiveness of this approach, illustrating its robust classification performance.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 2","pages":"94-99"},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091179","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 : 2024-03-03DOI: 10.1109/JMASS.2024.3396519
Xiangwei Bu;Ruining Luo;Humin Lei
We address the problem of tracking control for uncertain discrete-time systems with unknown and unavailable plant dynamics, aiming to achieve prescribed performance within a preset convergence time for tracking errors. Our proposed control protocol is independent of the knowledge of system dynamics or the utilization of approximators/estimators. Instead, we employ transformed errors to develop novel nonlinear functions for control feedback. Consequently, we establish a new estimation/approximation-free indirect stabilization framework that serves as a standard paradigm for discrete-time prescribed performance control synthesis. Finally, simulation results applied to the missile seeker stabilized platform demonstrate the effectiveness of our approach.
{"title":"Discrete-Time Estimation/Approximation-Avoidance Control With Prescribed Performance","authors":"Xiangwei Bu;Ruining Luo;Humin Lei","doi":"10.1109/JMASS.2024.3396519","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3396519","url":null,"abstract":"We address the problem of tracking control for uncertain discrete-time systems with unknown and unavailable plant dynamics, aiming to achieve prescribed performance within a preset convergence time for tracking errors. Our proposed control protocol is independent of the knowledge of system dynamics or the utilization of approximators/estimators. Instead, we employ transformed errors to develop novel nonlinear functions for control feedback. Consequently, we establish a new estimation/approximation-free indirect stabilization framework that serves as a standard paradigm for discrete-time prescribed performance control synthesis. Finally, simulation results applied to the missile seeker stabilized platform demonstrate the effectiveness of our approach.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"175-181"},"PeriodicalIF":0.0,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041373","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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