Pub Date : 2022-08-24DOI: 10.1109/IAI55780.2022.9976790
Xingshang Li, Fanjun Li, Shoujing Zheng, Qianwen Liu
The echo state network (ESN) is a special recurrent neural network, which is a powerful method of time series prediction. However, the traditional ESN with single reservoir cannot fully mine the feature information of complicated time series. In this article, a block-structured echo state network (BESN) with cascaded modules is proposed to solve this problem based on error reduction mechanism. In BESN, the external inputs and the outputs of the previous module form the inputs of the next adjacent module, and the prediction errors of the previous module are defined as the target outputs of the next module. Meanwhile, the number of modules is determined by a self-organizing method for BESN. Finally, the performance of BESN is tested on two benchmarks.
{"title":"Block-structured echo state network based on error reduction mechanism","authors":"Xingshang Li, Fanjun Li, Shoujing Zheng, Qianwen Liu","doi":"10.1109/IAI55780.2022.9976790","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976790","url":null,"abstract":"The echo state network (ESN) is a special recurrent neural network, which is a powerful method of time series prediction. However, the traditional ESN with single reservoir cannot fully mine the feature information of complicated time series. In this article, a block-structured echo state network (BESN) with cascaded modules is proposed to solve this problem based on error reduction mechanism. In BESN, the external inputs and the outputs of the previous module form the inputs of the next adjacent module, and the prediction errors of the previous module are defined as the target outputs of the next module. Meanwhile, the number of modules is determined by a self-organizing method for BESN. Finally, the performance of BESN is tested on two benchmarks.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"755 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122982324","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976722
Yuan Yu, Lin Pan, Jun-an Bao, Hao Tian
This study proposes an adaptive sliding mode control (SMC) strategy based on neural networks and backstepping method for trajectory tracking control of the underactuated unmanned surface vessel (USV). The controller is decomposed into two loops of kinematics and dynamics by using the back-stepping control. In the kinematics loop, the surge and sway reference velocities of USV are designed and regarded as virtual control laws to stabilize the position errors. In the dynamics loop, the SMC is used to design the control laws. To avoid chattering of SMC, the exponential approach rate is improved by using the arctangent function, which forms the sliding mode controller with the variable parameter approach rate. The neural network based on the minimum learning parameter method (MLP) is used to approximate the uncertain terms of the model to enhance the robustness of the system and reduce the computational complexity. The adaptive laws are proposed to compensate for the approximation errors of neural networks and disturbances. By constructing the Lyapunov function, it is demonstrated the proposed control scheme can guarantee the uniform final boundedness of all signals in the closed-loop system. Finally, simulation results on an underactuated USV further illustrate the effectiveness.
{"title":"Adaptive backstepping sliding mode control based on MLP neural network for trajectory tracking of USV","authors":"Yuan Yu, Lin Pan, Jun-an Bao, Hao Tian","doi":"10.1109/IAI55780.2022.9976722","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976722","url":null,"abstract":"This study proposes an adaptive sliding mode control (SMC) strategy based on neural networks and backstepping method for trajectory tracking control of the underactuated unmanned surface vessel (USV). The controller is decomposed into two loops of kinematics and dynamics by using the back-stepping control. In the kinematics loop, the surge and sway reference velocities of USV are designed and regarded as virtual control laws to stabilize the position errors. In the dynamics loop, the SMC is used to design the control laws. To avoid chattering of SMC, the exponential approach rate is improved by using the arctangent function, which forms the sliding mode controller with the variable parameter approach rate. The neural network based on the minimum learning parameter method (MLP) is used to approximate the uncertain terms of the model to enhance the robustness of the system and reduce the computational complexity. The adaptive laws are proposed to compensate for the approximation errors of neural networks and disturbances. By constructing the Lyapunov function, it is demonstrated the proposed control scheme can guarantee the uniform final boundedness of all signals in the closed-loop system. Finally, simulation results on an underactuated USV further illustrate the effectiveness.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"1998 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125715361","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}
To solve the difficulty of rapid and accurate detection of component content in the rare earth extraction process, a component content modeling method combining mechanism model and error compensation model based on just-in-time learning (JITL) was proposed. Considering the different dynamic characteristics of each section, the extraction section is simplified using the segmented-aggregation method, and the mechanism model of the rare earth extraction process based on material balance is established; in view of the error caused by the simplification of the model and the characteristics of some rare earth solutions with color features, the color features of rare earth solution samples are extracted by machine vision technology, and the error compensation model of the mechanism model is established by the just-in-time learning algorithm. Through the experimental verification of the field sample data of the praseodymium/neodymium (Pr/Nd) extraction process, the results show that the modeling method proposed in this paper is suitable for rapid and accurate detection of elemental component content in the rare earth extraction process with ionic color features.
{"title":"Prediction of Element Component Content Based on Mechanism Analysis and Error Compensation","authors":"Rongxiu Lu, Biao Deng, Kanghao Ding, Hui Yang, Jianyong Zhu, Hongliang Liu","doi":"10.1109/IAI55780.2022.9976647","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976647","url":null,"abstract":"To solve the difficulty of rapid and accurate detection of component content in the rare earth extraction process, a component content modeling method combining mechanism model and error compensation model based on just-in-time learning (JITL) was proposed. Considering the different dynamic characteristics of each section, the extraction section is simplified using the segmented-aggregation method, and the mechanism model of the rare earth extraction process based on material balance is established; in view of the error caused by the simplification of the model and the characteristics of some rare earth solutions with color features, the color features of rare earth solution samples are extracted by machine vision technology, and the error compensation model of the mechanism model is established by the just-in-time learning algorithm. Through the experimental verification of the field sample data of the praseodymium/neodymium (Pr/Nd) extraction process, the results show that the modeling method proposed in this paper is suitable for rapid and accurate detection of elemental component content in the rare earth extraction process with ionic color features.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"241 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114049631","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976779
J. Viola, Yangquan Chen
The first order plus delay time (FOPDT) systems, are a class of commonly used model family to describe thermal or temperature control systems which comprise 80% of all control tasks. The delay $(L)$ over the time constant $(tau)$ is known as “relative time delay”. In practice, this relative time delay may change over different tasks or missions. How to design a smart controller that can be aware of this change and can still seek to achieve the optimal performance, is the main theme of this paper. We follow our previous achievements in self-optimizing control (SOC) using a globalized constrained Nelder-Mead (GCNM) on-line optimization algorithm. We first reviewed our SOC framework under GCNM for FOPDT and using extensive examples we shall how the SOC module is made aware of changes in relative time delay
{"title":"Relative-Time-Delay-Aware Self-Optimizing-Control for First-Order-Plus-Delay-Time Systems","authors":"J. Viola, Yangquan Chen","doi":"10.1109/IAI55780.2022.9976779","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976779","url":null,"abstract":"The first order plus delay time (FOPDT) systems, are a class of commonly used model family to describe thermal or temperature control systems which comprise 80% of all control tasks. The delay $(L)$ over the time constant $(tau)$ is known as “relative time delay”. In practice, this relative time delay may change over different tasks or missions. How to design a smart controller that can be aware of this change and can still seek to achieve the optimal performance, is the main theme of this paper. We follow our previous achievements in self-optimizing control (SOC) using a globalized constrained Nelder-Mead (GCNM) on-line optimization algorithm. We first reviewed our SOC framework under GCNM for FOPDT and using extensive examples we shall how the SOC module is made aware of changes in relative time delay","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"94 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115832537","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976690
Bitong Huai, Han Liu, Guo Xie, Youmin Zhang
As an important task of intelligent interpretation research, the object detection of remote sensing images still has many problems to be solved. In this paper, aiming at the characteristics of small-sized object and complex background, in order to solve the problem of poor effect of existing object detection algorithms when applied to remote sensing images, an object detection model of remote sensing images based on the improved Faster R-CNN model is proposed. Based on the original Faster R-CNN model, the feature extraction network VGG16 is improved by designing a feature fusion module. In order to verify the effectiveness of the model in this paper, it is used to carry out experiments on NWPU VHR-10 and DOTA datasets, and mAP has reached 0.886 and 0.810 respectively, which was 6.9% and 11.6% higher than the original Faster R-CNN. The experimental results show that our method effectively improves the object detection effect of remote sensing images, and achieves good results in remote sensing images with small-sized object and complex background.
{"title":"Intelligent Interpretation of High-resolution Remote Sensing Images based on Deep Learning","authors":"Bitong Huai, Han Liu, Guo Xie, Youmin Zhang","doi":"10.1109/IAI55780.2022.9976690","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976690","url":null,"abstract":"As an important task of intelligent interpretation research, the object detection of remote sensing images still has many problems to be solved. In this paper, aiming at the characteristics of small-sized object and complex background, in order to solve the problem of poor effect of existing object detection algorithms when applied to remote sensing images, an object detection model of remote sensing images based on the improved Faster R-CNN model is proposed. Based on the original Faster R-CNN model, the feature extraction network VGG16 is improved by designing a feature fusion module. In order to verify the effectiveness of the model in this paper, it is used to carry out experiments on NWPU VHR-10 and DOTA datasets, and mAP has reached 0.886 and 0.810 respectively, which was 6.9% and 11.6% higher than the original Faster R-CNN. The experimental results show that our method effectively improves the object detection effect of remote sensing images, and achieves good results in remote sensing images with small-sized object and complex background.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"448 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122486446","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976794
Longyan Li, C. Ning
This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study.
{"title":"Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid","authors":"Longyan Li, C. Ning","doi":"10.1109/IAI55780.2022.9976794","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976794","url":null,"abstract":"This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"30 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132762501","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976633
Shengping Yu, Wenju Zhou, Jun Liu
As an important information display tool closely related to people's daily life, the liquid crystal display (LCD) has become an inseparable part of people's lives. In the manufacturing process of LCD, screen defect detection is an indispensable step which directly affects the yield and quality of LCD. In order to improve the accuracy and efficiency of defect detection for LCD screen, this paper proposes a novel defect detection method for LCD based on machine vision. Firstly, preprocessing operations including grayscale, binarization, filtering and dilation are used to reduce background noise and enhance the useful features of LCD screens. Secondly, the maximum connected region (MCR) and minimum external rectangle (MER) are adopted to initially locate the position of the LCD screen; Then, the affine transformation is introduced to correct the tilted screen and horizontal projection (HP) and vertical projection (VP) are presented to extract the LCD screen. Finally, a regional template matching algorithm is proposed to detect defects of LCD screens. Experiments show the effectiveness and robustness of the proposed method.
{"title":"A Novel Defect Detection Method of Liquid Crystal Display Based on Machine Vision","authors":"Shengping Yu, Wenju Zhou, Jun Liu","doi":"10.1109/IAI55780.2022.9976633","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976633","url":null,"abstract":"As an important information display tool closely related to people's daily life, the liquid crystal display (LCD) has become an inseparable part of people's lives. In the manufacturing process of LCD, screen defect detection is an indispensable step which directly affects the yield and quality of LCD. In order to improve the accuracy and efficiency of defect detection for LCD screen, this paper proposes a novel defect detection method for LCD based on machine vision. Firstly, preprocessing operations including grayscale, binarization, filtering and dilation are used to reduce background noise and enhance the useful features of LCD screens. Secondly, the maximum connected region (MCR) and minimum external rectangle (MER) are adopted to initially locate the position of the LCD screen; Then, the affine transformation is introduced to correct the tilted screen and horizontal projection (HP) and vertical projection (VP) are presented to extract the LCD screen. Finally, a regional template matching algorithm is proposed to detect defects of LCD screens. Experiments show the effectiveness and robustness of the proposed method.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116229365","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976497
Zhengrun Zhao, Zhi-wen Chen, Qiao Deng, Peng-Fei Tang, Tao Peng
The efficient operation of the cooling source system depends on a reasonable control strategy, and accurate cooling load prediction provides important guidance for optimal control. As there are numerous variables that affect the prediction of cooling loads, many cooling load prediction methods try to exploit the variables in the temporal domain. However, the correlations between the variables are not reasonably utilized by many methods. To exploit the implicit information of the data and obtain an accurate cooling load prediction, the correlative temporal graph convolutional network (CTGCN) is used to predict the cooling load, which can extracted the correlation information and the temporal information. Notably, the correlations between the key variables that affect the cooling load prediction are used for the correlation graph construction, which provides guidance for correlation information extraction. Some traditional prediction methods are compared to prove the effectiveness of the proposed method in the field of cooling load prediction. The results show that the proposed model has great practical value in cooling load prediction.
{"title":"Cooling load prediction based on correlative temporal graph convolutional network","authors":"Zhengrun Zhao, Zhi-wen Chen, Qiao Deng, Peng-Fei Tang, Tao Peng","doi":"10.1109/IAI55780.2022.9976497","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976497","url":null,"abstract":"The efficient operation of the cooling source system depends on a reasonable control strategy, and accurate cooling load prediction provides important guidance for optimal control. As there are numerous variables that affect the prediction of cooling loads, many cooling load prediction methods try to exploit the variables in the temporal domain. However, the correlations between the variables are not reasonably utilized by many methods. To exploit the implicit information of the data and obtain an accurate cooling load prediction, the correlative temporal graph convolutional network (CTGCN) is used to predict the cooling load, which can extracted the correlation information and the temporal information. Notably, the correlations between the key variables that affect the cooling load prediction are used for the correlation graph construction, which provides guidance for correlation information extraction. Some traditional prediction methods are compared to prove the effectiveness of the proposed method in the field of cooling load prediction. The results show that the proposed model has great practical value in cooling load prediction.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130703835","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}
Accurate prediction of air-conditioning cooling load is not only beneficial to control energy consumption and improve energy efficiency, but also provides a theoretical basis and data support for energy conservation and emission reduction. Aiming at the problems that large deviation of original data and low prediction accuracy in air-conditioning cooling load prediction. An air-conditioning cooling load prediction model combined with time convolutional network (TCN) combined with permutation entropy (PE), savitzky-golay (SG) and variational mode decomposition (VMD) is proposed in this paper. Firstly, Pearson correlation coefficient is used to analyze historical data. Secondly, the complex multi-component cooling load signal is decomposed into multiple single-component amplitudes and frequency modulation (AFM) signals by VMD. The PE is used to quantitatively determine the noise content of each component after VMD decomposition, the high noise component is directly removed, the low noise component is smoothly processed by the SG smoothing method, then, the signal is reconstructed. Finally, the TCN model of air-conditioning cooling load prediction is established. The experimental results show that the prediction accuracy of the hybrid model is significantly improved compared with the conventional models.
{"title":"Cooling load prediction of air-conditioning based on VMD-TCN using PE-SG algorithm","authors":"Ning He, Lijun Zhang, Liqiang Liu, Danlei Chu, Mengrui Zhang, Cheng Qian","doi":"10.1109/IAI55780.2022.9976778","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976778","url":null,"abstract":"Accurate prediction of air-conditioning cooling load is not only beneficial to control energy consumption and improve energy efficiency, but also provides a theoretical basis and data support for energy conservation and emission reduction. Aiming at the problems that large deviation of original data and low prediction accuracy in air-conditioning cooling load prediction. An air-conditioning cooling load prediction model combined with time convolutional network (TCN) combined with permutation entropy (PE), savitzky-golay (SG) and variational mode decomposition (VMD) is proposed in this paper. Firstly, Pearson correlation coefficient is used to analyze historical data. Secondly, the complex multi-component cooling load signal is decomposed into multiple single-component amplitudes and frequency modulation (AFM) signals by VMD. The PE is used to quantitatively determine the noise content of each component after VMD decomposition, the high noise component is directly removed, the low noise component is smoothly processed by the SG smoothing method, then, the signal is reconstructed. Finally, the TCN model of air-conditioning cooling load prediction is established. The experimental results show that the prediction accuracy of the hybrid model is significantly improved compared with the conventional models.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"5 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128784584","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 : 2022-08-24DOI: 10.1109/IAI55780.2022.9976827
Jun-an Bao, Lin Pan, Jiying Wang, Yuan Yu, Hao Tian
As to the trajectory tracking control problem of unmanned surface vessel(USV) under environment disturbance, this study proposes a nonsingular fast terminal sliding mode controller which is based on an extended state observer(ESO). Firstly, an auxiliary velocity vector is proposed to further simplify the USV models. Secondly, this study adopts an ESO to estimate the total unknown environment disturbance, where the observed value should be compensated into the controller. Thirdly, based ESO, a novel nonsingular fast terminal sliding mode(NFTSM) controller is introduced to guarantee the good tracking performance of the system. Finally, the convergence stability is verified by Lyapunov function and a simulation experiment is introduced to prove the effectiveness and reliability of the developed scheme.
{"title":"Nonsigular fast terminal sliding mode control based on Extended state observer for trajectory tracking of USV","authors":"Jun-an Bao, Lin Pan, Jiying Wang, Yuan Yu, Hao Tian","doi":"10.1109/IAI55780.2022.9976827","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976827","url":null,"abstract":"As to the trajectory tracking control problem of unmanned surface vessel(USV) under environment disturbance, this study proposes a nonsingular fast terminal sliding mode controller which is based on an extended state observer(ESO). Firstly, an auxiliary velocity vector is proposed to further simplify the USV models. Secondly, this study adopts an ESO to estimate the total unknown environment disturbance, where the observed value should be compensated into the controller. Thirdly, based ESO, a novel nonsingular fast terminal sliding mode(NFTSM) controller is introduced to guarantee the good tracking performance of the system. Finally, the convergence stability is verified by Lyapunov function and a simulation experiment is introduced to prove the effectiveness and reliability of the developed scheme.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126239464","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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