Traditional proportional-integral-derivative (PID) controllers have achieved widespread success in industrial applications. However, the nonlinearity and uncertainty of practical systems cannot be ignored, even though most of the existing research on PID controllers is focused on linear systems. Therefore, developing a PID controller with learning ability is of great significance for complex nonlinear systems. This article proposes a deterministic learning-based advanced PID controller for robot manipulator systems with uncertainties. The introduction of neural networks (NNs) overcomes the upper limit of the traditional PID feedback mechanism's capability. The proposed control scheme not only guarantees system stability and tracking error convergence but also provides a simple way to choose the three parameters of PID by setting the proportional coefficients. Under the partial persistent excitation (PE) condition, the closed-loop system unknown dynamics of robot manipulator systems are accurately approximated by NNs. Based on the acquired knowledge from the stable control process, a learning PID controller is developed to further improve overall control performance, while overcoming the problem of repeated online weight updates. Simulation studies and physical experiments demonstrate the validity and practicality of the proposed strategy discussed in this article.
{"title":"Deterministic Learning-Based Neural PID Control for Nonlinear Robotic Systems","authors":"Qinchen Yang;Fukai Zhang;Cong Wang","doi":"10.1109/JAS.2024.124224","DOIUrl":"https://doi.org/10.1109/JAS.2024.124224","url":null,"abstract":"Traditional proportional-integral-derivative (PID) controllers have achieved widespread success in industrial applications. However, the nonlinearity and uncertainty of practical systems cannot be ignored, even though most of the existing research on PID controllers is focused on linear systems. Therefore, developing a PID controller with learning ability is of great significance for complex nonlinear systems. This article proposes a deterministic learning-based advanced PID controller for robot manipulator systems with uncertainties. The introduction of neural networks (NNs) overcomes the upper limit of the traditional PID feedback mechanism's capability. The proposed control scheme not only guarantees system stability and tracking error convergence but also provides a simple way to choose the three parameters of PID by setting the proportional coefficients. Under the partial persistent excitation (PE) condition, the closed-loop system unknown dynamics of robot manipulator systems are accurately approximated by NNs. Based on the acquired knowledge from the stable control process, a learning PID controller is developed to further improve overall control performance, while overcoming the problem of repeated online weight updates. Simulation studies and physical experiments demonstrate the validity and practicality of the proposed strategy discussed in this article.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140605971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zheng Chen;Shizhao Zhou;Chong Shen;Litong Lyu;Junhui Zhang;Bin Yao
Hydraulic manipulators are usually applied in heavy-load and harsh operation tasks. However, when faced with a complex operation, the traditional proportional-integral-derivative (PID) control may not meet requirements for high control performance. Model-based full-state-feedback control is an effective alternative, but the states of a hydraulic manipulator are not always available and reliable in practical applications, particularly the joint angular velocity measurement. Considering that it is not suitable to obtain the velocity signal directly from differentiating of position measurement, the low-pass filtering is commonly used, but it will definitely restrict the closed-loop band-width of the whole system. To avoid this problem and realize better control performance, this paper proposes a novel observer-based adaptive robust controller (obARC) for a multi-joint hydraulic manipulator subjected to both parametric uncertainties and the lack of accurate velocity measurement. Specifically, a nonlinear adaptive observer is first designed to handle the lack of velocity measurement with the consideration of parametric uncertainties. Then, the adaptive robust control is developed to compensate for the dynamic uncertainties, and the close-loop system robust stability is theoretically proved under the observation and control errors. Finally, comparative experiments are carried out to show that the designed controller can achieve a performance improvement over the traditional methods, specifically yielding better control accuracy owing to the closed-loop band-width breakthrough, which is limited by low-pass filtering in full-state-feedback control.
{"title":"Observer-Based Adaptive Robust Precision Motion Control of a Multi-Joint Hydraulic Manipulator","authors":"Zheng Chen;Shizhao Zhou;Chong Shen;Litong Lyu;Junhui Zhang;Bin Yao","doi":"10.1109/JAS.2024.124209","DOIUrl":"https://doi.org/10.1109/JAS.2024.124209","url":null,"abstract":"Hydraulic manipulators are usually applied in heavy-load and harsh operation tasks. However, when faced with a complex operation, the traditional proportional-integral-derivative (PID) control may not meet requirements for high control performance. Model-based full-state-feedback control is an effective alternative, but the states of a hydraulic manipulator are not always available and reliable in practical applications, particularly the joint angular velocity measurement. Considering that it is not suitable to obtain the velocity signal directly from differentiating of position measurement, the low-pass filtering is commonly used, but it will definitely restrict the closed-loop band-width of the whole system. To avoid this problem and realize better control performance, this paper proposes a novel observer-based adaptive robust controller (obARC) for a multi-joint hydraulic manipulator subjected to both parametric uncertainties and the lack of accurate velocity measurement. Specifically, a nonlinear adaptive observer is first designed to handle the lack of velocity measurement with the consideration of parametric uncertainties. Then, the adaptive robust control is developed to compensate for the dynamic uncertainties, and the close-loop system robust stability is theoretically proved under the observation and control errors. Finally, comparative experiments are carried out to show that the designed controller can achieve a performance improvement over the traditional methods, specifically yielding better control accuracy owing to the closed-loop band-width breakthrough, which is limited by low-pass filtering in full-state-feedback control.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay (FFSR) networks. An FFSR is located between the sensor and the remote filter to forward the measurement. In the successive relay, two cooperative relay nodes are adopted to forward the signals alternatively, thereby existing switching characteristics and inter-relay interferences (IRI). Since the filter-and-forward scheme is employed, the signal received by the relay is retransmitted after it passes through a linear filter, The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays. First, a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR. Then, novel filter structures with switching parameters are constructed for both FFSR and stochastic systems. With the help of the inductive method, filtering error covariances are presented in the form of coupled difference equations. Next, the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances. Moreover, the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance. Finally, the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.
{"title":"Recursive Filtering for Stochastic Systems with Filter-and-Forward Successive Relays","authors":"Hailong Tan;Bo Shen;Qi Li;Hongjian Liu","doi":"10.1109/JAS.2023.124110","DOIUrl":"https://doi.org/10.1109/JAS.2023.124110","url":null,"abstract":"In this paper, the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay (FFSR) networks. An FFSR is located between the sensor and the remote filter to forward the measurement. In the successive relay, two cooperative relay nodes are adopted to forward the signals alternatively, thereby existing switching characteristics and inter-relay interferences (IRI). Since the filter-and-forward scheme is employed, the signal received by the relay is retransmitted after it passes through a linear filter, The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays. First, a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR. Then, novel filter structures with switching parameters are constructed for both FFSR and stochastic systems. With the help of the inductive method, filtering error covariances are presented in the form of coupled difference equations. Next, the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances. Moreover, the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance. Finally, the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shaoying Wang;Zidong Wang;Hongli Dong;Yun Chen;Guoping Lu
This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises, multiple missing measurements as well as the dynamic event-triggered transmission scheme. The multiple missing measurements are characterized through random variables that obey some given probability distributions, and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable. Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense. To this end, the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers, thus the original design issue is reformulated as that of the augmented system. Subsequently, we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters. With the aid of two well-defined matrix difference equations, we not only obtain upper bounds on filtering error covariances, but also minimize those bounds via carefully designing gain parameters. Finally, an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.
{"title":"Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems: Tackling Multiplicative Noises and Missing Measurements","authors":"Shaoying Wang;Zidong Wang;Hongli Dong;Yun Chen;Guoping Lu","doi":"10.1109/JAS.2024.124338","DOIUrl":"https://doi.org/10.1109/JAS.2024.124338","url":null,"abstract":"This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises, multiple missing measurements as well as the dynamic event-triggered transmission scheme. The multiple missing measurements are characterized through random variables that obey some given probability distributions, and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable. Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense. To this end, the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers, thus the original design issue is reformulated as that of the augmented system. Subsequently, we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters. With the aid of two well-defined matrix difference equations, we not only obtain upper bounds on filtering error covariances, but also minimize those bounds via carefully designing gain parameters. Finally, an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140555933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiao Xue;Xiangning Yu;Deyu Zhou;Xiao Wang;Chongke Bi;Shufang Wang;Fei-Yue Wang
Powered by advanced information industry and intelligent technology, more and more complex systems are exhibiting characteristics of the cyber-physical-social systems (CPSS). And human factors have become crucial in the operations of complex social systems. Traditional mechanical analysis and social simulations alone are powerless for analyzing complex social systems. Against this backdrop, computational experiments have emerged as a new method for quantitative analysis of complex social systems by combining social simulation (e.g., ABM), complexity science, and domain knowledge. However, in the process of applying computational experiments, the construction of experiment system not only considers a large number of artificial society models, but also involves a large amount of data and knowledge. As a result, how to integrate various data, model and knowledge to achieve a running experiment system has become a key challenge. This paper proposes an integrated design framework of computational experiment system, which is composed of four parts: generation of digital subject, generation of digital object, design of operation engine, and construction of experiment system. Finally, this paper outlines a typical case study of coal mine emergency management to verify the validity of the proposed framework.
{"title":"Computational Experiments for Complex Social Systems: Integrated Design of Experiment System","authors":"Xiao Xue;Xiangning Yu;Deyu Zhou;Xiao Wang;Chongke Bi;Shufang Wang;Fei-Yue Wang","doi":"10.1109/JAS.2023.123639","DOIUrl":"https://doi.org/10.1109/JAS.2023.123639","url":null,"abstract":"Powered by advanced information industry and intelligent technology, more and more complex systems are exhibiting characteristics of the cyber-physical-social systems (CPSS). And human factors have become crucial in the operations of complex social systems. Traditional mechanical analysis and social simulations alone are powerless for analyzing complex social systems. Against this backdrop, computational experiments have emerged as a new method for quantitative analysis of complex social systems by combining social simulation (e.g., ABM), complexity science, and domain knowledge. However, in the process of applying computational experiments, the construction of experiment system not only considers a large number of artificial society models, but also involves a large amount of data and knowledge. As a result, how to integrate various data, model and knowledge to achieve a running experiment system has become a key challenge. This paper proposes an integrated design framework of computational experiment system, which is composed of four parts: generation of digital subject, generation of digital object, design of operation engine, and construction of experiment system. Finally, this paper outlines a typical case study of coal mine emergency management to verify the validity of the proposed framework.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140555932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization problems. The past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems (HEPs). The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer simulations. Moreover, it is hard to traverse the huge search space within reasonable resource as problem dimension increases. Traditional evolutionary algorithms (EAs) tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory results. To reduce such evaluations, many novel surrogate-assisted algorithms emerge to cope with HEPs in recent years. Yet there lacks a thorough review of the state of the art in this specific and important area. This paper provides a comprehensive survey of these evolutionary algorithms for HEPs. We start with a brief introduction to the research status and the basic concepts of HEPs. Then, we present surrogate-assisted evolutionary algorithms for HEPs from four main aspects. We also give comparative results of some representative algorithms and application examples. Finally, we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.
进化计算是一个发展迅速的领域，相关算法已成功用于解决现实世界中的各种优化问题。在过去的十年中，进化计算在解决一类具有挑战性的优化问题--高维昂贵问题（HEPs）--方面也取得了快速进展。由于使用耗时的物理实验或计算机模拟，对其目标适合度的评估需要昂贵的资源。此外，随着问题维度的增加，很难在合理的资源范围内穿越巨大的搜索空间。传统的进化算法（EA）往往无法胜任 HEPs 的求解，因为它们在取得令人满意的结果之前需要进行多次这样昂贵的评估。为了减少这种评估，近年来出现了许多新型的代用辅助算法来应对 HEPs。然而，在这一特殊而重要的领域，缺乏对最新技术的全面回顾。本文全面考察了这些针对 HEP 的进化算法。我们首先简要介绍了 HEP 的研究现状和基本概念。然后，我们从四个主要方面介绍了用于 HEP 的代理辅助进化算法。我们还给出了一些代表性算法的比较结果和应用实例。最后，我们指出了推进 HEP 进化优化算法进展的挑战和几个有前途的方向。
{"title":"Evolutionary Optimization Methods for High-Dimensional Expensive Problems: A Survey","authors":"MengChu Zhou;Meiji Cui;Dian Xu;Shuwei Zhu;Ziyan Zhao;Abdullah Abusorrah","doi":"10.1109/JAS.2024.124320","DOIUrl":"https://doi.org/10.1109/JAS.2024.124320","url":null,"abstract":"Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization problems. The past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems (HEPs). The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer simulations. Moreover, it is hard to traverse the huge search space within reasonable resource as problem dimension increases. Traditional evolutionary algorithms (EAs) tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory results. To reduce such evaluations, many novel surrogate-assisted algorithms emerge to cope with HEPs in recent years. Yet there lacks a thorough review of the state of the art in this specific and important area. This paper provides a comprehensive survey of these evolutionary algorithms for HEPs. We start with a brief introduction to the research status and the basic concepts of HEPs. Then, we present surrogate-assisted evolutionary algorithms for HEPs from four main aspects. We also give comparative results of some representative algorithms and application examples. Finally, we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140556000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D measurements. The algorithm for restoring the original 3D hyperspectral images (HSIs) from compressive measurements is pivotal in the imaging process. Early approaches painstakingly designed networks to directly map compressive measurements to HSIs, resulting in the lack of interpretability without exploiting the imaging priors. While some recent works have introduced the deep unfolding framework for explainable reconstruction, the performance of these methods is still limited by the weak information transmission between iterative stages. In this paper, we propose a Memory-Augmented deep Unfolding Network, termed MAUN, for explainable and accurate HSI reconstruction. Specifically, MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm, introducing an extra momentum incorporation step for each iteration to alleviate the information loss. Moreover, to exploit the high correlation of intermediate images from neighboring iterations, we customize a cross-stage transformer (CSFormer) as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features, which is the first attempt to model the long-distance dependencies between iteration stages. Extensive experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and metrically. Our code is publicly available at https://github.com/HuQ1an/MAUN.
{"title":"MAUN: Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction","authors":"Qian Hu;Jiayi Ma;Yuan Gao;Junjun Jiang;Yixuan Yuan","doi":"10.1109/JAS.2024.124362","DOIUrl":"https://doi.org/10.1109/JAS.2024.124362","url":null,"abstract":"Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D measurements. The algorithm for restoring the original 3D hyperspectral images (HSIs) from compressive measurements is pivotal in the imaging process. Early approaches painstakingly designed networks to directly map compressive measurements to HSIs, resulting in the lack of interpretability without exploiting the imaging priors. While some recent works have introduced the deep unfolding framework for explainable reconstruction, the performance of these methods is still limited by the weak information transmission between iterative stages. In this paper, we propose a Memory-Augmented deep Unfolding Network, termed MAUN, for explainable and accurate HSI reconstruction. Specifically, MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm, introducing an extra momentum incorporation step for each iteration to alleviate the information loss. Moreover, to exploit the high correlation of intermediate images from neighboring iterations, we customize a cross-stage transformer (CSFormer) as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features, which is the first attempt to model the long-distance dependencies between iteration stages. Extensive experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and metrically. Our code is publicly available at https://github.com/HuQ1an/MAUN.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140556009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dear Editor, An adaptive consensus control algorithm for uncertain multi-agent systems (MAS), capable of guaranteeing unified prescribed performance, is presented in this letter. Unlike many existing prescribed performance related works, the developed control exhibits some features. Firstly, a distributed prescribed time observer is introduced so that not only each follower is able to estimate the leader's signal within a predetermined time, but also the control design for each agent is independent with its neighbors, making the original coupled relationship between agents removed. Secondly, by constructing some nonlinear transformations and parameter-oriented asymmetric barrier function, the problem of ensuring different kinds of prescribed performance behaviors can be converted into the selection of design parameters, making the control redesign not needed and different mission requirements satisfied under a fixed control framework. According to the Lyapunov method, it is shown that not only the closed-loop signals are bounded, but also the consensus errors can be evolved within the prescribed boundaries. Simulations are provided to verify the effectiveness of the proposed approach.
{"title":"Adaptive Consensus of Uncertain Multi-Agent Systems with Unified Prescribed Performance","authors":"Kun Li;Kai Zhao;Yongduan Song","doi":"10.1109/JAS.2023.123723","DOIUrl":"https://doi.org/10.1109/JAS.2023.123723","url":null,"abstract":"Dear Editor, An adaptive consensus control algorithm for uncertain multi-agent systems (MAS), capable of guaranteeing unified prescribed performance, is presented in this letter. Unlike many existing prescribed performance related works, the developed control exhibits some features. Firstly, a distributed prescribed time observer is introduced so that not only each follower is able to estimate the leader's signal within a predetermined time, but also the control design for each agent is independent with its neighbors, making the original coupled relationship between agents removed. Secondly, by constructing some nonlinear transformations and parameter-oriented asymmetric barrier function, the problem of ensuring different kinds of prescribed performance behaviors can be converted into the selection of design parameters, making the control redesign not needed and different mission requirements satisfied under a fixed control framework. According to the Lyapunov method, it is shown that not only the closed-loop signals are bounded, but also the consensus errors can be evolved within the prescribed boundaries. Simulations are provided to verify the effectiveness of the proposed approach.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10500725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140556010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines the stabilization issue of extended chained nonholonomic systems (ECNSs) with external disturbance. Unlike the existing approaches, we transform the considered system into a fully actuated system (FAS) model, simplifying the stabilizing controller design. We implement a separate controller design and propose exponential stabilization controller and finite-time stabilization controller under finite-time disturbance observer (FTDO) for the two system inputs. In addition, we discuss the specifics of global stabilization control design. Our approach demonstrates that two system states exponentially or asymptotically converge to zero under the provided switching stabilization control strategy, while all other system states converge to zero within a finite time.
{"title":"Stabilization Controller of an Extended Chained Nonholonomic System With Disturbance: An FAS Approach","authors":"Zhongcai Zhang;Guangren Duan","doi":"10.1109/JAS.2023.124098","DOIUrl":"https://doi.org/10.1109/JAS.2023.124098","url":null,"abstract":"This study examines the stabilization issue of extended chained nonholonomic systems (ECNSs) with external disturbance. Unlike the existing approaches, we transform the considered system into a fully actuated system (FAS) model, simplifying the stabilizing controller design. We implement a separate controller design and propose exponential stabilization controller and finite-time stabilization controller under finite-time disturbance observer (FTDO) for the two system inputs. In addition, we discuss the specifics of global stabilization control design. Our approach demonstrates that two system states exponentially or asymptotically converge to zero under the provided switching stabilization control strategy, while all other system states converge to zero within a finite time.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140556012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dear Editor, This letter presents an organoid segmentation model based on multi-axis attention with convolution parallel block. MACPNet adeptly captures dynamic dependencies within bright-field microscopy images, improving global modeling beyond conventional UNet. It excels in sparse global interactions and concurrent computation, yielding enhanced segmentation. MACPNet stands out for its prowess in multi-scale data capture, aligned with diverse distance dependencies inherent in organoid images. Experimental results show that the proposed model outperforms several state-of-the-art methods as well as multiple baseline models in accurate organoid segmentation.
{"title":"Multi-Axis Attention with Convolution Parallel Block for Organoid Segmentation","authors":"Pengwei Hu;Xun Deng;Feng Tan;Lun Hu","doi":"10.1109/JAS.2023.124026","DOIUrl":"https://doi.org/10.1109/JAS.2023.124026","url":null,"abstract":"Dear Editor, This letter presents an organoid segmentation model based on multi-axis attention with convolution parallel block. MACPNet adeptly captures dynamic dependencies within bright-field microscopy images, improving global modeling beyond conventional UNet. It excels in sparse global interactions and concurrent computation, yielding enhanced segmentation. MACPNet stands out for its prowess in multi-scale data capture, aligned with diverse distance dependencies inherent in organoid images. Experimental results show that the proposed model outperforms several state-of-the-art methods as well as multiple baseline models in accurate organoid segmentation.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":null,"pages":null},"PeriodicalIF":11.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10500717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140555873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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