Constrained multi-objective optimization problems (CMOPs) generally contain multiple constraints, which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions, thus they propose serious challenges for solvers. Among all constraints, some constraints are highly correlated with optimal feasible regions; thus they can provide effective help to find feasible Pareto front. However, most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints, and do not consider judging the relations among constraints and do not utilize the information from promising single constraints. Therefore, this paper attempts to identify promising single constraints and utilize them to help solve CMOPs. To be specific, a CMOP is transformed into a multi-tasking optimization problem, where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively. Besides, an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships. Moreover, an improved tentative method is designed to find and transfer useful knowledge among tasks. Experimental results on three benchmark test suites and 11 real-world problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.
{"title":"Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems","authors":"Kangjia Qiao;Jing Liang;Kunjie Yu;Xuanxuan Ban;Caitong Yue;Boyang Qu;Ponnuthurai Nagaratnam Suganthan","doi":"10.1109/JAS.2024.124545","DOIUrl":"https://doi.org/10.1109/JAS.2024.124545","url":null,"abstract":"Constrained multi-objective optimization problems (CMOPs) generally contain multiple constraints, which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions, thus they propose serious challenges for solvers. Among all constraints, some constraints are highly correlated with optimal feasible regions; thus they can provide effective help to find feasible Pareto front. However, most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints, and do not consider judging the relations among constraints and do not utilize the information from promising single constraints. Therefore, this paper attempts to identify promising single constraints and utilize them to help solve CMOPs. To be specific, a CMOP is transformed into a multi-tasking optimization problem, where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively. Besides, an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships. Moreover, an improved tentative method is designed to find and transfer useful knowledge among tasks. Experimental results on three benchmark test suites and 11 real-world problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1819-1835"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729936","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}
This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation. For the distributed estimation structure under consideration, an estimation center is not necessary, and the estimator derives its information from itself and neighboring nodes, which fuses the state vector and the measurement vector. In an effort to cut down data conflicts in communication networks, the stochastic communication protocol (SCP) is employed so that the output signals from sensors can be selected. Additionally, a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data. On this basis, sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error. Finally, a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.
{"title":"Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols","authors":"Weiwei Sun;Xinci Gao;Lusong Ding;Xiangyu Chen","doi":"10.1109/JAS.2023.124161","DOIUrl":"https://doi.org/10.1109/JAS.2023.124161","url":null,"abstract":"This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation. For the distributed estimation structure under consideration, an estimation center is not necessary, and the estimator derives its information from itself and neighboring nodes, which fuses the state vector and the measurement vector. In an effort to cut down data conflicts in communication networks, the stochastic communication protocol (SCP) is employed so that the output signals from sensors can be selected. Additionally, a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data. On this basis, sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error. Finally, a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1865-1876"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729901","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}
Partial least squares (PLS) model is the most typical data-driven method for quality-related industrial tasks like soft sensor. However, only linear relations are captured between the input and output data in the PLS. It is difficult to obtain the remaining nonlinear information in the residual subspaces, which may deteriorate the prediction performance in complex industrial processes. To fully utilize data information in PLS residual subspaces, a deep residual PLS (DRPLS) framework is proposed for quality prediction in this paper. Inspired by deep learning, DRPLS is designed by stacking a number of PLSs successively, in which the input residuals of the previous PLS are used as the layer connection. To enhance representation, nonlinear function is applied to the input residuals before using them for stacking highlevel PLS. For each PLS, the output parts are just the output residuals from its previous PLS. Finally, the output prediction is obtained by adding the results of each PLS. The effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.
{"title":"A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process","authors":"Xiaofeng Yuan;Weiwei Xu;Yalin Wang;Chunhua Yang;Weihua Gui","doi":"10.1109/JAS.2024.124578","DOIUrl":"https://doi.org/10.1109/JAS.2024.124578","url":null,"abstract":"Partial least squares (PLS) model is the most typical data-driven method for quality-related industrial tasks like soft sensor. However, only linear relations are captured between the input and output data in the PLS. It is difficult to obtain the remaining nonlinear information in the residual subspaces, which may deteriorate the prediction performance in complex industrial processes. To fully utilize data information in PLS residual subspaces, a deep residual PLS (DRPLS) framework is proposed for quality prediction in this paper. Inspired by deep learning, DRPLS is designed by stacking a number of PLSs successively, in which the input residuals of the previous PLS are used as the layer connection. To enhance representation, nonlinear function is applied to the input residuals before using them for stacking highlevel PLS. For each PLS, the output parts are just the output residuals from its previous PLS. Finally, the output prediction is obtained by adding the results of each PLS. The effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1777-1785"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729882","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}
Ljubo Vlacic;Hailong Huang;Mariagrazia Dotoli;Yutong Wang;Petros A. Ioannou;Lili Fan;Xingxia Wang;Raffaele Carli;Chen Lv;Lingxi Li;Xiaoxiang Na;Qing-Long Han;Fei-Yue Wang
Automation has come a long way since the early days of mechanization, i.e., the process of working exclusively by hand or using animals to work with machinery. The rise of steam engines and water wheels represented the first generation of industry, which is now called Industry 1.0. Subsequently, Industry 2.0 witnessed the development of electric power and assembly lines. Later on, programmable logic controllers and Human Machine Interfaces (HMI) were the new productivity tools in Industry 3.0, which enabled precise and consistent production. In recent years, Industry 4.0 absorbed the latest technologies of Internet of Things (IoT), Artificial Intelligence (AI), and big data, making production processes integrated, interconnected, and smart. Nowadays, Industry 5.0 has been proposed, which emphasizes human-centric automation. Specifically, the new concept of automation in Industry 5.0, named Automation 5.0, is no longer about how to create machinery to replace humans. Instead, it aims to reach organic interactions and cooperation between humans and machines, meeting the goal of “6S” - Safety, Security, Sustainability, Sensitivity, Service, and Smartness [1]–[4] - and the overall objective of deploying automation for the better, human-friendly, and smarter industry.
{"title":"Automation 5.0: The Key to Systems Intelligence and Industry 5.0","authors":"Ljubo Vlacic;Hailong Huang;Mariagrazia Dotoli;Yutong Wang;Petros A. Ioannou;Lili Fan;Xingxia Wang;Raffaele Carli;Chen Lv;Lingxi Li;Xiaoxiang Na;Qing-Long Han;Fei-Yue Wang","doi":"10.1109/JAS.2024.124635","DOIUrl":"https://doi.org/10.1109/JAS.2024.124635","url":null,"abstract":"Automation has come a long way since the early days of mechanization, i.e., the process of working exclusively by hand or using animals to work with machinery. The rise of steam engines and water wheels represented the first generation of industry, which is now called Industry 1.0. Subsequently, Industry 2.0 witnessed the development of electric power and assembly lines. Later on, programmable logic controllers and Human Machine Interfaces (HMI) were the new productivity tools in Industry 3.0, which enabled precise and consistent production. In recent years, Industry 4.0 absorbed the latest technologies of Internet of Things (IoT), Artificial Intelligence (AI), and big data, making production processes integrated, interconnected, and smart. Nowadays, Industry 5.0 has been proposed, which emphasizes human-centric automation. Specifically, the new concept of automation in Industry 5.0, named Automation 5.0, is no longer about how to create machinery to replace humans. Instead, it aims to reach organic interactions and cooperation between humans and machines, meeting the goal of “6S” - Safety, Security, Sustainability, Sensitivity, Service, and Smartness [1]–[4] - and the overall objective of deploying automation for the better, human-friendly, and smarter industry.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1723-1727"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605757","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729900","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}
Dear Editor, This letter addresses the challenge of forecasting the motion of real-world vessels over an extended period with a limited amount of available data. By employing stochastic differential equation (SDE) modeling, we integrate both deterministic and stochastic components of the available information. Subsequently, we establish a recursive prediction methodology based on Bayes' rule to update the model state when new measurements are received. Furthermore, we develop a stochastic model tailored specifically to vessel dynamics and introduce an approximation method to tackle computational complexities. Finally, we present an application example and conduct a comparative experiment to validate the effectiveness and superiority of the proposed method.
{"title":"A Probabilistic Approach for Predicting Vessel Motion","authors":"Qi Hu;Jingyi Liu;Zongyu Zuo","doi":"10.1109/JAS.2024.124536","DOIUrl":"https://doi.org/10.1109/JAS.2024.124536","url":null,"abstract":"Dear Editor, This letter addresses the challenge of forecasting the motion of real-world vessels over an extended period with a limited amount of available data. By employing stochastic differential equation (SDE) modeling, we integrate both deterministic and stochastic components of the available information. Subsequently, we establish a recursive prediction methodology based on Bayes' rule to update the model state when new measurements are received. Furthermore, we develop a stochastic model tailored specifically to vessel dynamics and introduce an approximation method to tackle computational complexities. Finally, we present an application example and conduct a comparative experiment to validate the effectiveness and superiority of the proposed method.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1877-1879"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605731","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729885","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}
Dear Editor, The existing control schemes for microgrids (MGs) face challenges in effectively addressing plugging in/out operations under uncertain power lines and faults. To tackle this issue, this letter proposes a novel scalable fault-tolerant control (FTC) strategy for DC MGs. By developing a structured Lyapunov matrix (SLM), a decoupled FTC method is introduced to mitigate the adverse effects of uncertain lines and nonuniform faults. Moreover, global stability is ensured by deriving local rules expressed as linear matrix inequalities (LMIs) that solely depend on local parameters. Each new distributed generation unit (DGU) introduces only one additional condition of this nature, allowing the stability rules to scale with topology changes. Finally, the effectiveness of the proposed scheme is illustrated through simulations conducted using the MATLAB/SimPowerSystems toolbox.
{"title":"A Novel Scalable Fault-Tolerant Control Design for DC Microgrids With Nonuniform Faults","authors":"Aimin Wang;Minrui Fei;Dajun Du;Yang Song","doi":"10.1109/JAS.2023.123918","DOIUrl":"https://doi.org/10.1109/JAS.2023.123918","url":null,"abstract":"Dear Editor, The existing control schemes for microgrids (MGs) face challenges in effectively addressing plugging in/out operations under uncertain power lines and faults. To tackle this issue, this letter proposes a novel scalable fault-tolerant control (FTC) strategy for DC MGs. By developing a structured Lyapunov matrix (SLM), a decoupled FTC method is introduced to mitigate the adverse effects of uncertain lines and nonuniform faults. Moreover, global stability is ensured by deriving local rules expressed as linear matrix inequalities (LMIs) that solely depend on local parameters. Each new distributed generation unit (DGU) introduces only one additional condition of this nature, allowing the stability rules to scale with topology changes. Finally, the effectiveness of the proposed scheme is illustrated through simulations conducted using the MATLAB/SimPowerSystems toolbox.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1886-1888"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605728","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729899","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}
The accelerated method in solving optimization problems has always been an absorbing topic. Based on the fixed-time (FxT) stability of nonlinear dynamical systems, we provide a unified approach for designing FxT gradient flows (FxTGFs). First, a general class of nonlinear functions in designing FxTGFs is provided. A unified method for designing first-order FxTGFs is shown under Polyak-Ljasiewicz inequality assumption, a weaker condition than strong convexity. When there exist both bounded and vanishing disturbances in the gradient flow, a specific class of nonsmooth robust FxTGFs with disturbance rejection is presented. Under the strict convexity assumption, Newton-based FxTGFs is given and further extended to solve time-varying optimization. Besides, the proposed FxTGFs are further used for solving equation-constrained optimization. Moreover, an FxT proximal gradient flow with a wide range of parameters is provided for solving nonsmooth composite optimization. To show the effectiveness of various FxTGFs, the static regret analyses for several typical FxTGFs are also provided in detail. Finally, the proposed FxTGFs are applied to solve two network problems, i.e., the network consensus problem and solving a system linear equations, respectively, from the perspective of optimization. Particularly, by choosing component-wisely sign-preserving functions, these problems can be solved in a distributed way, which extends the existing results. The accelerated convergence and robustness of the proposed FxTGFs are validated in several numerical examples stemming from practical applications.
{"title":"Fixed-Time Gradient Flows for Solving Constrained Optimization: A Unified Approach","authors":"Xinli Shi;Xiangping Xu;Guanghui Wen;Jinde Cao","doi":"10.1109/JAS.2023.124089","DOIUrl":"10.1109/JAS.2023.124089","url":null,"abstract":"The accelerated method in solving optimization problems has always been an absorbing topic. Based on the fixed-time (FxT) stability of nonlinear dynamical systems, we provide a unified approach for designing FxT gradient flows (FxTGFs). First, a general class of nonlinear functions in designing FxTGFs is provided. A unified method for designing first-order FxTGFs is shown under Polyak-Ljasiewicz inequality assumption, a weaker condition than strong convexity. When there exist both bounded and vanishing disturbances in the gradient flow, a specific class of nonsmooth robust FxTGFs with disturbance rejection is presented. Under the strict convexity assumption, Newton-based FxTGFs is given and further extended to solve time-varying optimization. Besides, the proposed FxTGFs are further used for solving equation-constrained optimization. Moreover, an FxT proximal gradient flow with a wide range of parameters is provided for solving nonsmooth composite optimization. To show the effectiveness of various FxTGFs, the static regret analyses for several typical FxTGFs are also provided in detail. Finally, the proposed FxTGFs are applied to solve two network problems, i.e., the network consensus problem and solving a system linear equations, respectively, from the perspective of optimization. Particularly, by choosing component-wisely sign-preserving functions, these problems can be solved in a distributed way, which extends the existing results. The accelerated convergence and robustness of the proposed FxTGFs are validated in several numerical examples stemming from practical applications.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1849-1864"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135041883","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 the context of multiple-target tracking and surveillance applications, this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multiple independently-steerable zooming cameras to effectively monitor a set of targets of interest. Each camera is dedicated to tracking a specific target or cluster of targets. The key innovation of this study, in comparison to existing approaches, lies in incorporating the zooming factor for the onboard cameras into the optimization problem. This enhancement offers greater flexibility during mission execution by allowing the autonomous agent to adjust the focal lengths of the on-board cameras, in exchange for varying real-world distances to the corresponding targets, thereby providing additional degrees of freedom to the optimization problem. The proposed optimization framework aims to strike a balance among various factors, including distance to the targets, verticality of viewpoints, and the required focal length for each camera. The primary focus of this paper is to establish the theoretical groundwork for addressing the non-convex nature of the optimization problem arising from these considerations. To this end, we develop an original convex approximation strategy. The paper also includes simulations of diverse scenarios, featuring varying numbers of onboard tracking cameras and target motion profiles, to validate the effectiveness of the proposed approach.
{"title":"Optimal Positioning Strategy for Multi-Camera Zooming Drones","authors":"Manuel Vargas;Carlos Vivas;Teodoro Alamo","doi":"10.1109/JAS.2024.124455","DOIUrl":"https://doi.org/10.1109/JAS.2024.124455","url":null,"abstract":"In the context of multiple-target tracking and surveillance applications, this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multiple independently-steerable zooming cameras to effectively monitor a set of targets of interest. Each camera is dedicated to tracking a specific target or cluster of targets. The key innovation of this study, in comparison to existing approaches, lies in incorporating the zooming factor for the onboard cameras into the optimization problem. This enhancement offers greater flexibility during mission execution by allowing the autonomous agent to adjust the focal lengths of the on-board cameras, in exchange for varying real-world distances to the corresponding targets, thereby providing additional degrees of freedom to the optimization problem. The proposed optimization framework aims to strike a balance among various factors, including distance to the targets, verticality of viewpoints, and the required focal length for each camera. The primary focus of this paper is to establish the theoretical groundwork for addressing the non-convex nature of the optimization problem arising from these considerations. To this end, we develop an original convex approximation strategy. The paper also includes simulations of diverse scenarios, featuring varying numbers of onboard tracking cameras and target motion profiles, to validate the effectiveness of the proposed approach.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1802-1818"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729935","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}
Weihao Song;Zidong Wang;Zhongkui Li;Jianan Wang;Qing-Long Han
The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance. The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation, cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many sample-based nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter, and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security. Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.
{"title":"Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication: Progress, Insights and Trends","authors":"Weihao Song;Zidong Wang;Zhongkui Li;Jianan Wang;Qing-Long Han","doi":"10.1109/JAS.2023.123588","DOIUrl":"https://doi.org/10.1109/JAS.2023.123588","url":null,"abstract":"The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance. The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation, cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many sample-based nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter, and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security. Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1539-1556"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315262","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}
Kun Jiang;Wenzhang Liu;Yuanda Wang;Lu Dong;Changyin Sun
Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning (MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable (MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience. Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.
{"title":"Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning","authors":"Kun Jiang;Wenzhang Liu;Yuanda Wang;Lu Dong;Changyin Sun","doi":"10.1109/JAS.2024.124281","DOIUrl":"https://doi.org/10.1109/JAS.2024.124281","url":null,"abstract":"Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning (MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable (MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience. Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1591-1604"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315240","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}
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