Article
Automation & Control Systems
Marcello Farina, Marco Rocca
Summary: In this paper, a novel algorithm based on linear matrix inequalities is proposed for the design of distributed controllers and state estimators for large-scale systems. The algorithm is inspired by linear quadratic regulators and Kalman filters. Compared to similar methods, the proposed scheme reduces the conservativeness caused by the approximations used for the covariance distributed iterative computation. The theoretical properties of the proposed scheme are thoroughly investigated, and its performances are compared to state-of-the-art schemes, demonstrating its potential.
EUROPEAN JOURNAL OF CONTROL
(2023)
Article
Computer Science, Theory & Methods
Hamza Ruzayqat, Neil K. Chada, Ajay Jasra
Summary: This article discusses the application of multilevel Monte Carlo in the estimation of normalizing constants, specifically focusing on the multilevel ensemble Kalman-Bucy filter (MLEnKBF) method. Numerical results are provided to validate the approach, and parameter estimation is tested on atmospheric science models such as the stochastic Lorenz 63 and 96 model.
STATISTICS AND COMPUTING
(2022)
Article
Automation & Control Systems
Chaofang Hu, Ge Qu, Hyo-Sang Shin, Antonios Tsourdos
Summary: This paper proposes a cooperative path planning method using multiple UAVs to track an unknown ground moving target. By forecasting target state, considering multiple priorities and objectives, and designing collision avoidance constraints, effective path planning is achieved.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Engineering, Multidisciplinary
Chenxu Liang, Wenchao Xue, Haitao Fang, Ran Zhang
Summary: This paper investigates the distributed state estimation problem for a class of discrete-time linear time-varying systems over a bearings-only sensor network. The novel fusion estimation algorithm is proposed to estimate the distance between the target and each sensor, with the mean square error matrix being taken into consideration. The refined distance estimation is then obtained by minimizing the mean square error matrix. Finally, the distributed Kalman filter based state estimation algorithm is proposed and its consistency and stability are rigorously proven. The numerical simulation results demonstrate the effectiveness of the proposed methods.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2023)
Article
Automation & Control Systems
Xuan-Zhi Zhu, David Cabecinhas, Wei Xie, Pedro Casau, Carlos Silvestre, Pedro Batista, Paulo Oliveira
Summary: This paper proposes a control strategy for trajectory tracking of a quadcopter, which is adaptable to parametric uncertainties and external disturbances. Simulation and experimental results validate the stability and robustness of the proposed strategy.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Automation & Control Systems
Zhidong Xu, Bo Ding, Tianping Zhang
Summary: This paper investigates the problem of event-based state and fault estimation for stochastic nonlinear systems with Markov packet dropout. By introducing fictitious noise, the fault is integrated into the system state, and a modified unscented Kalman filter (UKF) is proposed to estimate the state and fault. The stochastic stability of the proposed filter is also discussed, and two simulation results are presented to illustrate the performance of the proposed method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Lang Ma, Yu-Long Wang, Qing-Long Han
Summary: This paper focuses on cooperative target tracking of multiple autonomous surface vehicles (ASVs) under switching interaction topologies. A distributed extended state observer is designed to estimate unknown target dynamics and neighboring ASVs' dynamics. A novel kinematic controller is designed to take full advantage of known information and avoid approximation of virtual control vectors. A disturbance observer is presented to estimate unknown time-varying environmental disturbance. A distributed dynamic controller is designed to enable ASVs to cooperatively track the target based on received information from neighbors. The effectiveness of the derived results is demonstrated through analysis of cooperative target tracking performance for a system of five interacting ASVs.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Automation & Control Systems
Cong Zhang, Jiahu Qin, Chengzhen Yan, Yang Shi, Yaonan Wang, Man Li
Summary: This paper studies the problem of resilient distributed state estimation for moving robots over wireless sensor networks under deception attacks. A novel invariant extended Kalman filter-based approach is proposed to achieve resilient distributed state estimation by linearizing estimation errors, fusing local prior estimates, and using adaptive gains for correction. The performance of the approach is illustrated through convergence analysis and simulation experiments.
Article
Multidisciplinary Sciences
Kevin Course, Prasanth B. Nair
Summary: This study presents a state estimation technique based on approximate Bayesian approach, which learns the missing terms and state estimation in the mathematical model. It enables state estimation for physical systems with partially or completely unknown dynamical equations.
Article
Chemistry, Analytical
Francesco Alonge, Pasquale Cusumano, Filippo D'Ippolito, Giovanni Garraffa, Patrizia Livreri, Antonino Sferlazza
Summary: A novel scheme for velocity and position estimation in a UWB range-based localization system is proposed in this study. The suggested strategy overcomes two main problems typically encountered in localization systems, namely, suitability for use in environments where the GPS signal is not present or might fail, and no requirement for accelerometer measurements in the localization task. Experimentally, a suitable Kalman-Bucy filter is designed and compared with a particle filter, showcasing the features of the two algorithms for localization usage. Further experimental tests on a developed test setup confirm the effectiveness of the proposed approach.
Article
Automation & Control Systems
Bosen Lian, Yan Wan, Ya Zhang, Mushuang Liu, Frank L. Lewis, Tianyou Chai
Summary: In this article, we propose a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation. We address the issues of low convergence speed and limited sensing range and target mobility in existing Kalman filters. Our simulations and comparative studies demonstrate the effectiveness and superiority of the proposed DKCF.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Mechanical
Neha Aswal, Subhamoy Sen, Laurent Mevel
Summary: This study proposes a Bayesian filtering based approach for accurate structural damage detection in the presence of sensor faults. The approach utilizes a switching filtering strategy for sensor fault detection and a joint state-parameter estimation approach for damage detection. The performance of the proposed approach is tested on numerical and laboratory models, and it is found to provide accurate alarms and decision-making even when faulty sensors are present.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Fengzeng Zhu, Ju H. Park, Li Peng
Summary: This paper presents a distributed state estimation method for nonlinear systems over sensor networks with Semi-Markovian switching topologies (S-MSTs). An adaptive event-triggered quantization scheme (AETQS) is developed to reduce the communication and computation burden for bandwidth-constrained sensor networks. The optimal disturbance attenuation level, initial triggering thresholds, and elapsed-time-dependent distributed filter gains can be determined by addressing a convex optimization problem. Two numerical examples are presented to verify the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
(2022)
Article
Computer Science, Information Systems
Sining Cheng, Jiaxian Qin, Yuanyuan Chen, Mingzhu Li
Summary: The study discussed the technology of moving target detection based on UAV vision, using the YOLOv3 algorithm to analyze and track vehicle violations. Experimental results show that compared to traditional pattern recognition, moving target detection based on UAV vision has higher accuracy and recognition speed, as well as being more time-saving.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Article
Automation & Control Systems
Li Li, Mingyang Fan, Yuanqing Xia, Cui Zhu
Summary: This paper focuses on distributed fusion estimation for a multi-sensor nonlinear stochastic system, proposing an event-trigger mechanism and unscented Kalman filters for fusion estimation. It establishes boundedness conditions for fusion estimation error covariance through a recursive algorithm and trigger threshold. An ideal compromise between communication rate and estimation performance is achieved.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
