Pub Date : 2024-11-11DOI: 10.1109/TCST.2024.3495392
{"title":"2023-2024 Index IEEE Transactions on Control Systems Technology Vol. 32","authors":"","doi":"10.1109/TCST.2024.3495392","DOIUrl":"https://doi.org/10.1109/TCST.2024.3495392","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2500-2545"},"PeriodicalIF":4.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
In this brief, we propose a sequential convex programming (SCP) framework for minimizing the terminal state dispersion of a stochastic dynamical system about a prescribed destination—an important property in high-risk contexts such as spacecraft landing. Our proposed approach seeks to minimize the conditional value-at-risk (CVaR) of the dispersion, thereby shifting the probability distribution away from the tails. This approach provides an optimization framework that is not overly conservative and can accurately capture more information about true distribution, compared with methods which consider only the expected value, or robust optimization methods. The main contribution of this brief is to present an approach that: 1) establishes an optimization problem with CVaR dispersion cost 2) approximated with one of the two novel surrogates which is then 3) solved using an efficient SCP algorithm. In 2), two approximation methods, a sampling approximation (SA) and a symmetric polytopic approximation (SPA), are introduced for transforming the stochastic objective function into a deterministic form. The accuracy of the SA increases with sample size at the cost of problem size and computation time. To overcome this, we introduce the SPA, which avoids sampling by using an alternative approximation and thus offers significant computational benefits. Monte Carlo simulations indicate that our proposed approaches minimize the CVaR of the dispersion successfully.
{"title":"Dispersion Sensitive Optimal Control: A Conditional Value-at-Risk-Based Tail Flattening Approach via Sequential Convex Programming","authors":"Kazuya Echigo;Oliver Sheridan;Samuel Buckner;Behçet Açıkmeşe","doi":"10.1109/TCST.2024.3427910","DOIUrl":"10.1109/TCST.2024.3427910","url":null,"abstract":"In this brief, we propose a sequential convex programming (SCP) framework for minimizing the terminal state dispersion of a stochastic dynamical system about a prescribed destination—an important property in high-risk contexts such as spacecraft landing. Our proposed approach seeks to minimize the conditional value-at-risk (CVaR) of the dispersion, thereby shifting the probability distribution away from the tails. This approach provides an optimization framework that is not overly conservative and can accurately capture more information about true distribution, compared with methods which consider only the expected value, or robust optimization methods. The main contribution of this brief is to present an approach that: 1) establishes an optimization problem with CVaR dispersion cost 2) approximated with one of the two novel surrogates which is then 3) solved using an efficient SCP algorithm. In 2), two approximation methods, a sampling approximation (SA) and a symmetric polytopic approximation (SPA), are introduced for transforming the stochastic objective function into a deterministic form. The accuracy of the SA increases with sample size at the cost of problem size and computation time. To overcome this, we introduce the SPA, which avoids sampling by using an alternative approximation and thus offers significant computational benefits. Monte Carlo simulations indicate that our proposed approaches minimize the CVaR of the dispersion successfully.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2468-2475"},"PeriodicalIF":4.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and uncertainty of behavior. Control methodology in this new world must be designed for resilience and must have the ability to withstand, react, and respond to both physical faults and cyber-induced threats [1] . Understanding system resilience under adverse conditions requires studying control performance and how cyber infrastructure can integrate with and support the overall resilience of the system.
{"title":"Guest Editorial: Special section on Resilient Control of Cyber-Physical Power and Energy Systems","authors":"Veronica Adetola;Thomas Edgar;Sai Pushpak Nandanoori;Quanyan Zhu;Craig Rieger;Masoud Abbaszadeh","doi":"10.1109/TCST.2024.3403515","DOIUrl":"https://doi.org/10.1109/TCST.2024.3403515","url":null,"abstract":"Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and uncertainty of behavior. Control methodology in this new world must be designed for resilience and must have the ability to withstand, react, and respond to both physical faults and cyber-induced threats \u0000<xref>[1]</xref>\u0000. Understanding system resilience under adverse conditions requires studying control performance and how cyber infrastructure can integrate with and support the overall resilience of the system.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 5","pages":"1688-1691"},"PeriodicalIF":4.9,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2024-08-06DOI: 10.1109/TCST.2024.3433229
Abraham P. Vinod;Sleiman Safaoui;Tyler H. Summers;Nobuyuki Yoshikawa;Stefano Di Cairano
We propose a decentralized, multiagent motion planner that guarantees the probabilistic safety of a team subject to stochastic uncertainty in the agent model and environment. Our scalable approach generates safe motion plans in real-time using off-the-shelf, single-agent reinforcement learning (RL) rendered safe using distributionally robust, convex optimization and buffered Voronoi cells. We guarantee the recursive feasibility of the mean trajectories and mitigate the conservativeness using a temporal discounting of safety. We show in simulation that our approach generates safe and high-performant trajectories as compared to existing approaches, and further validate these observations in physical experiments using drones.
{"title":"Decentralized, Safe, Multiagent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning","authors":"Abraham P. Vinod;Sleiman Safaoui;Tyler H. Summers;Nobuyuki Yoshikawa;Stefano Di Cairano","doi":"10.1109/TCST.2024.3433229","DOIUrl":"10.1109/TCST.2024.3433229","url":null,"abstract":"We propose a decentralized, multiagent motion planner that guarantees the probabilistic safety of a team subject to stochastic uncertainty in the agent model and environment. Our scalable approach generates safe motion plans in real-time using off-the-shelf, single-agent reinforcement learning (RL) rendered safe using distributionally robust, convex optimization and buffered Voronoi cells. We guarantee the recursive feasibility of the mean trajectories and mitigate the conservativeness using a temporal discounting of safety. We show in simulation that our approach generates safe and high-performant trajectories as compared to existing approaches, and further validate these observations in physical experiments using drones.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2492-2499"},"PeriodicalIF":4.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1109/TCST.2024.3426300
Jorge Val Ledesma;Rafał Wisniewski;Carsten S. Kallesøe;Agisilaos Tsouvalas
Reinforcement learning (RL) is a widely used control technique that finds an optimal policy using the feedback of its actions. The search for the optimal policy requires that the system explores a broad region of the state space. This search puts at risk the safe operation, since some of the explored regions might be near the physical system limits. Implementing learning methods in industrial applications is limited because of its uncertain behavior when finding an optimal policy. This work proposes an RL control algorithm with a filter that supervises the safety of the exploration based on a nominal model. The performance of this safety filter is increased by modeling the uncertainty with a Gaussian process (GP) regression. This method is applied to the optimization of the management of a water distribution network (WDN) with an elevated reservoir; the management objectives are to regulate the tank filling while maintaining an adequate water turnover. The proposed method is validated in a laboratory setup that emulates the hydraulic features of a WDN.
{"title":"Water Age Control for Water Distribution Networks via Safe Reinforcement Learning","authors":"Jorge Val Ledesma;Rafał Wisniewski;Carsten S. Kallesøe;Agisilaos Tsouvalas","doi":"10.1109/TCST.2024.3426300","DOIUrl":"https://doi.org/10.1109/TCST.2024.3426300","url":null,"abstract":"Reinforcement learning (RL) is a widely used control technique that finds an optimal policy using the feedback of its actions. The search for the optimal policy requires that the system explores a broad region of the state space. This search puts at risk the safe operation, since some of the explored regions might be near the physical system limits. Implementing learning methods in industrial applications is limited because of its uncertain behavior when finding an optimal policy. This work proposes an RL control algorithm with a filter that supervises the safety of the exploration based on a nominal model. The performance of this safety filter is increased by modeling the uncertainty with a Gaussian process (GP) regression. This method is applied to the optimization of the management of a water distribution network (WDN) with an elevated reservoir; the management objectives are to regulate the tank filling while maintaining an adequate water turnover. The proposed method is validated in a laboratory setup that emulates the hydraulic features of a WDN.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2332-2343"},"PeriodicalIF":4.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1109/TCST.2024.3430708
Shuang Feng;Ricardo de Castro;Iman Ebrahimi
This article proposes a control barrier function (CBF) approach for fast charging and discharging of batteries under temperature, state of charge (SoC), and terminal voltage constraints. To improve numerical efficiency, we derive a cascade CBF formulation, which divides this safety problem into multiple layers that are easier to formulate and implement. The proposed algorithm exhibits a computational speed that is seven times faster than the model predictive control (MPC) and 3.6 times faster than the traditional single-layer (central) CBF. In the charging scenario, experimental results indicate that the proposed algorithm reduces charging time by 20% in comparison to traditional constant current, constant voltage (CC-CV) methods without violating electro-thermal safety constraints. The discharging experiment illustrates that the cascade CBF effectively limits the battery’s performance to ensure compliance with safety constraints.
{"title":"Safe Battery Control Using Cascade-Control-Barrier Functions","authors":"Shuang Feng;Ricardo de Castro;Iman Ebrahimi","doi":"10.1109/TCST.2024.3430708","DOIUrl":"10.1109/TCST.2024.3430708","url":null,"abstract":"This article proposes a control barrier function (CBF) approach for fast charging and discharging of batteries under temperature, state of charge (SoC), and terminal voltage constraints. To improve numerical efficiency, we derive a cascade CBF formulation, which divides this safety problem into multiple layers that are easier to formulate and implement. The proposed algorithm exhibits a computational speed that is seven times faster than the model predictive control (MPC) and 3.6 times faster than the traditional single-layer (central) CBF. In the charging scenario, experimental results indicate that the proposed algorithm reduces charging time by 20% in comparison to traditional constant current, constant voltage (CC-CV) methods without violating electro-thermal safety constraints. The discharging experiment illustrates that the cascade CBF effectively limits the battery’s performance to ensure compliance with safety constraints.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2344-2358"},"PeriodicalIF":4.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1109/TCST.2024.3429908
Jianping Lin;Gray C. Thomas;Nikhil V. Divekar;Vamsi Peddinti;Robert D. Gregg
Various backdrivable lower limb exoskeletons have demonstrated the electromechanical capability to assist volitional motions of able-bodied users and people with mild to moderate gait disorders, but there does not exist a control framework that can be deployed on any joint(s) to assist any activity of daily life in a provably stable manner. This article presents the modular, multitask optimal energy shaping (M-TOES) framework, which uses a convex, data-driven optimization to train an analytical control model to instantaneously determine assistive joint torques across activities for any lower limb exoskeleton joint configuration. The presented modular energy basis is sufficiently descriptive to fit normative human joint torques (given normative feedback from signals available to a given joint configuration) across sit-stand transitions, stair ascent/descent, ramp ascent/descent, and level walking at different speeds. We evaluated controllers for four joint configurations (unilateral/bilateral and hip/knee) of the modular backdrivable lower limb unloading exoskeleton (M-BLUE) exoskeleton on eight able-bodied users navigating a multiactivity circuit. The two unilateral conditions significantly lowered overall muscle activation across all tasks and subjects (p $mathbf {lt }$ 0.001). In contrast, bilateral configurations had a minimal impact, possibly attributable to device weight and physical constraints.
{"title":"A Modular Framework for Task-Agnostic, Energy Shaping Control of Lower Limb Exoskeletons","authors":"Jianping Lin;Gray C. Thomas;Nikhil V. Divekar;Vamsi Peddinti;Robert D. Gregg","doi":"10.1109/TCST.2024.3429908","DOIUrl":"10.1109/TCST.2024.3429908","url":null,"abstract":"Various backdrivable lower limb exoskeletons have demonstrated the electromechanical capability to assist volitional motions of able-bodied users and people with mild to moderate gait disorders, but there does not exist a control framework that can be deployed on any joint(s) to assist any activity of daily life in a provably stable manner. This article presents the modular, multitask optimal energy shaping (M-TOES) framework, which uses a convex, data-driven optimization to train an analytical control model to instantaneously determine assistive joint torques across activities for any lower limb exoskeleton joint configuration. The presented modular energy basis is sufficiently descriptive to fit normative human joint torques (given normative feedback from signals available to a given joint configuration) across sit-stand transitions, stair ascent/descent, ramp ascent/descent, and level walking at different speeds. We evaluated controllers for four joint configurations (unilateral/bilateral and hip/knee) of the modular backdrivable lower limb unloading exoskeleton (M-BLUE) exoskeleton on eight able-bodied users navigating a multiactivity circuit. The two unilateral conditions significantly lowered overall muscle activation across all tasks and subjects (p\u0000<inline-formula> <tex-math>$mathbf {lt }$ </tex-math></inline-formula>\u00000.001). In contrast, bilateral configurations had a minimal impact, possibly attributable to device weight and physical constraints.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2359-2375"},"PeriodicalIF":4.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1109/TCST.2024.3430128
Gian Carlo Maffettone;Lorenzo Liguori;Eduardo Palermo;Mario Di Bernardo;Maurizio Porfiri
Many new methodologies for the control of large-scale multiagent systems are based on macroscopic representations of the system dynamics, in the form of continuum approximations of large ensembles. These techniques, developed in the limit case of an infinite number of agents, are usually validated only through numerical simulations. Here, we introduce a mixed reality setup for testing swarm robotics techniques, focusing on the macroscopic collective motion of robotic swarms. This hybrid apparatus combines real differential drive robots and virtual agents to create a heterogeneous swarm of tunable size. We also extend continuification-based control methods for swarms to higher dimensions and experimentally assess their validity in the new platform. Our study demonstrates the effectiveness of the platform for conducting large-scale swarm robotics experiments, and it contributes new theoretical insights into control algorithms exploiting continuification approaches.
{"title":"Mixed Reality Environment and High-Dimensional Continuification Control for Swarm Robotics","authors":"Gian Carlo Maffettone;Lorenzo Liguori;Eduardo Palermo;Mario Di Bernardo;Maurizio Porfiri","doi":"10.1109/TCST.2024.3430128","DOIUrl":"10.1109/TCST.2024.3430128","url":null,"abstract":"Many new methodologies for the control of large-scale multiagent systems are based on macroscopic representations of the system dynamics, in the form of continuum approximations of large ensembles. These techniques, developed in the limit case of an infinite number of agents, are usually validated only through numerical simulations. Here, we introduce a mixed reality setup for testing swarm robotics techniques, focusing on the macroscopic collective motion of robotic swarms. This hybrid apparatus combines real differential drive robots and virtual agents to create a heterogeneous swarm of tunable size. We also extend continuification-based control methods for swarms to higher dimensions and experimentally assess their validity in the new platform. Our study demonstrates the effectiveness of the platform for conducting large-scale swarm robotics experiments, and it contributes new theoretical insights into control algorithms exploiting continuification approaches.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2484-2491"},"PeriodicalIF":4.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614946","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
Pub Date : 2024-07-26DOI: 10.1109/TCST.2024.3430130
Zeyu Guo;Jun Yang;Shihua Li;Zuo Wang
This article presents a position-based visual servo approach to allow a quadrotor to visually land on a moving platform, addressing perception delay and disturbances using only sampled-data output feedback. Without the platform’s model prior, the relative position of the quadrotor and the platform is determined by capturing the AprilTag on the platform by an onboard camera. The limitation in the camera’s sampling frequency yields only discrete output with time delay, emphasizing the requirement for a sampled-data method, since continuous system theory is not applicable in this scenario. In addition, disturbances arising from the unknown platform motion, wind resistance, and attitude tracking errors are also unavoidable. To mitigate these issues, a sampled-data time-delay extended state observer (TDESO)-based predictor is developed, capable of actively predicting the current states and disturbances. Using these predictions, a composite sampled-data controller is devised that incorporates disturbance feedforward compensation, thus enhancing the system’s robustness against disturbances. Rigorous Lyapunov analysis is provided, offering a guarantee that the states of the sampled-data control system converge asymptotically to a bounded region, even in the presence of perception delay and disturbances. The effectiveness and practicality of the proposed algorithm are supported by simulations and experimental results.
{"title":"Robust Visual Landing Control of Quadrotor on a Moving Platform: A Sampled-Data Approach With Delayed Output and Disturbances","authors":"Zeyu Guo;Jun Yang;Shihua Li;Zuo Wang","doi":"10.1109/TCST.2024.3430130","DOIUrl":"10.1109/TCST.2024.3430130","url":null,"abstract":"This article presents a position-based visual servo approach to allow a quadrotor to visually land on a moving platform, addressing perception delay and disturbances using only sampled-data output feedback. Without the platform’s model prior, the relative position of the quadrotor and the platform is determined by capturing the AprilTag on the platform by an onboard camera. The limitation in the camera’s sampling frequency yields only discrete output with time delay, emphasizing the requirement for a sampled-data method, since continuous system theory is not applicable in this scenario. In addition, disturbances arising from the unknown platform motion, wind resistance, and attitude tracking errors are also unavoidable. To mitigate these issues, a sampled-data time-delay extended state observer (TDESO)-based predictor is developed, capable of actively predicting the current states and disturbances. Using these predictions, a composite sampled-data controller is devised that incorporates disturbance feedforward compensation, thus enhancing the system’s robustness against disturbances. Rigorous Lyapunov analysis is provided, offering a guarantee that the states of the sampled-data control system converge asymptotically to a bounded region, even in the presence of perception delay and disturbances. The effectiveness and practicality of the proposed algorithm are supported by simulations and experimental results.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2283-2297"},"PeriodicalIF":4.9,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-18DOI: 10.1109/TCST.2024.3425211
Ahmed M. Elsergany;Mamoun F. Abdel-Hafez;Mohammad A. Jaradat
This article presents a novel direct filtering approach for loosely coupled global positioning system (GPS) and inertial navigation system (INS) integration. The proposed model is established based on utilizing the full nonlinear INS state equations in a direct configuration while including vehicle orientation through a unit-quaternion representation. A novel augmented quaternion unscented Kalman filter (AQUKF) is developed and proposed to address the direct nonlinear estimation of vehicle states for outdoor vehicle localization while preserving the non-Euclidean geometry of unit-quaternions. The proposed filter is experimentally validated under full GPS coverage as well as prolonged GPS outages. Results obtained in this article show that the proposed filter outperforms other existing solutions in various experimental testing scenarios.
本文针对松散耦合的全球定位系统（GPS）和惯性导航系统（INS）集成提出了一种新颖的直接滤波方法。所提议的模型是在直接配置中利用完整的非线性 INS 状态方程的基础上建立的，同时通过单位四元数表示法将车辆方位包括在内。开发并提出了一种新颖的增强四元数无特征卡尔曼滤波器（AQUKF），用于解决室外车辆定位的车辆状态直接非线性估计问题，同时保留了单位四元数的非欧几里得几何特性。实验验证了所提出的滤波器在 GPS 全面覆盖以及 GPS 长期中断的情况下的有效性。本文获得的结果表明，在各种实验测试场景中，所提出的滤波器优于其他现有解决方案。
{"title":"Novel Augmented Quaternion UKF for Enhanced Loosely Coupled GPS/INS Integration","authors":"Ahmed M. Elsergany;Mamoun F. Abdel-Hafez;Mohammad A. Jaradat","doi":"10.1109/TCST.2024.3425211","DOIUrl":"10.1109/TCST.2024.3425211","url":null,"abstract":"This article presents a novel direct filtering approach for loosely coupled global positioning system (GPS) and inertial navigation system (INS) integration. The proposed model is established based on utilizing the full nonlinear INS state equations in a direct configuration while including vehicle orientation through a unit-quaternion representation. A novel augmented quaternion unscented Kalman filter (AQUKF) is developed and proposed to address the direct nonlinear estimation of vehicle states for outdoor vehicle localization while preserving the non-Euclidean geometry of unit-quaternions. The proposed filter is experimentally validated under full GPS coverage as well as prolonged GPS outages. Results obtained in this article show that the proposed filter outperforms other existing solutions in various experimental testing scenarios.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2321-2331"},"PeriodicalIF":4.9,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10602523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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