Distributed H<sub>∞</sub>-Consensus Filtering for Attitude Tracking Using Ground-Based Radars

Qu, Huifang; Yang, Fang; Han, Qing‐Long; Zhang, Yilian

doi:10.1109/tcyb.2019.2901631

Cited by 16 publications

(5 citation statements)

References 41 publications

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“…By doing so, the consensus of the estimation performance can be achieved. From (19), it can be seen that the weight of the mesh is in fact determined via summing up the weights for all the particles which lie within the mesh. In view of the fact that each sensor node can generate particles independently, the applicability and feasibility of the mesh-based consensus method are significantly enhanced.…”

Section: Determine a L[h]mentioning

confidence: 99%

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Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Liu

Coombes

Liu

2023

IEEE Trans. on Signal and Inf. Process. over Networks

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Section: Determine a L[h]mentioning

confidence: 99%

“…via the information exchange and the consensus process. Thus, consensus-based filtering methods have been proposed in different frameworks such as the minimum variance filter [17], [18] and the robust filter [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Liu

Coombes

Liu

2023

IEEE Trans. on Signal and Inf. Process. over Networks

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“…During the event-triggered intervals [t h i , t h+1 i ), assuming that the neighbors of the rigid body i execute û * j = u * j (t h j ). According to (11) and (12), it can be easily obtained that…”

Section: B Dynamic Event-triggered Mechanismmentioning

confidence: 99%

Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks with Unknown Dynamics

Jin¹,

Mao²,

Kocarev³

et al. 2022

Preprint

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In this paper, an event-triggered Reinforcement Learning (RL) method is proposed for the optimal attitude consensus of multiple rigid body networks with unknown dynamics. Firstly, the consensus error is constructed through the attitude dynamics. According to the Bellman optimality principle, the implicit form of the optimal controller and the corresponding Hamilton-Jacobi-Bellman (HJB) equations are obtained. Because of the augmented system, the optimal controller can be obtained directly without relying on the system dynamics. Secondly, the self-triggered mechanism is applied to reduce the computing and communication burden when updating the controller. In order to address the problem that the HJB equations are difficult to solve analytically, a RL method which only requires measurement data at the event-triggered instants is proposed. For each agent, only one neural network is designed to approximate the optimal value function. Each neural network is updated only at the event-triggered instants. Meanwhile, the Uniformly Ultimately Bounded (UUB) of the closed-loop system is obtained, and the Zeno behavior is also avoided. Finally, the simulation results on a multiple rigid body network demonstrate the validity of the proposed method.

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“…To mitigate these challenges, it is imperative to design an efficient filtering scheme for the ASC system. Presently, there are three widely used filtering strategies: the distributed Kalman filter (DKF) (Chen et al, 2022; Ge et al, 2020; Yan et al, 2020a; Zhang et al, 2020), distributed H ∞ filtering (DHF) (Ge and Han, 2015; Qu et al, 2021; Yang et al, 2019), and distributed set-membership estimation (DSME) (Ge et al, 2019; Ma et al, 2017; Song et al, 2013). It is crucial to understand the rationale behind selecting these techniques and the benefits they offer in addressing the ASC system’s unique challenges.…”

Section: Introductionmentioning

confidence: 99%

Distributed set-membership estimation for automated straddle carriers using smart sensors

Chen,

Zhang,

Xia

et al. 2024

Transactions of the Institute of Measurement and Control

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Considering the harsh environment of the port, automated straddle carriers, characterized by their large size, tall frame, and high center of gravity, may experience instability during steering and transportation due to inaccurate state estimation. Thus, this paper explores state estimation techniques for automated straddle carriers utilizing smart sensors which are capable of data measurement and processing. First, using the steering principles and lateral characteristics of automated straddle carriers, a dynamic linear model is established based on Newton’s second law of motion. Then, in order to enhance the reliability and flexibility of state estimation, a distributed smart sensor network structure is introduced. In addition, considering the challenge of unknown-but-bounded noise and the precision demands of the considered automated straddle carrier, a modified distributed set-membership estimation algorithm is proposed and is derived sufficient conditions for the existence of the estimation set for the considered automated straddle carriers. Finally, the effectiveness and superiority of the proposed method are demonstrated by performance analyses.

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Distributed H_∞-Consensus Filtering for Attitude Tracking Using Ground-Based Radars

Cited by 16 publications

References 41 publications

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks with Unknown Dynamics

Distributed set-membership estimation for automated straddle carriers using smart sensors

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Distributed H∞-Consensus Filtering for Attitude Tracking Using Ground-Based Radars

Cited by 16 publications

References 41 publications

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks with Unknown Dynamics

Distributed set-membership estimation for automated straddle carriers using smart sensors

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Distributed H_∞-Consensus Filtering for Attitude Tracking Using Ground-Based Radars