Recursive filtering of networked nonlinear systems: a survey

Mao, Jingyang; Sun, Yonghe; Yi, Xiaojian; Liu, Hongjian; Ding, Derui

doi:10.1080/00207721.2020.1868615

Cited by 197 publications

(71 citation statements)

References 111 publications

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“…Lastly, we give two numerical examples to prove the effectiveness of the proposed method; simulation results show that this method meets the expected requirement well, realizes the synchronization of the master-slave DNNs, and saves the limited network resources effectively. Considering that there are many unpredictable factors in the real operating environment, such as strong signal attenuation and cyberattacks [39], the synchronization and state estimation…”

Section: Discussionmentioning

confidence: 99%

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

Wang

et al. 2021

Complexity

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This paper discusses the adaptive event-triggered synchronization problem of a class of neural networks (NNs) with time-varying delay and actuator saturation. First, in view of the limited communication channel capacity of the network system and unnecessary data transmission in the NCSs, an adaptive event-triggered scheme (AETS) is introduced to reduce the network load and improve network utilization. Second, under the AETS, the synchronization error model of the delayed master-slave synchronization system is constructed with actuator saturation. Third, based on Lyapunov–Krasovskii functional (LKF), a new sufficient criterion to guarantee the asymptotic stability of the synchronization error system is derived. Moreover, by solving the stability criterion expressed in the form of a set of linear matrix inequalities (LMIs), some necessary parameters of the system are obtained. At last, two examples are expressed to demonstrate the feasibility of this method.

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Section: Discussionmentioning

confidence: 99%

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

Wang

et al. 2021

Complexity

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“…According to the expression of (20), it shows the relation of Δu k+1 and Δu k . However, the relation of Δu k and e k is not clear, and it plays an important role in our next analysis.…”

Section: Design Of Average-iterative Learning Controllermentioning

confidence: 99%

“…Coincidentally, Bu et al made a more in-depth study on data-driven ILC and solved the problem of data quantization and limited sensor output [17][18][19]. Bandwidth issues [20] and cyberattacks also increase the difficulty of achieving multiagent consistency control. In [21], a method of dynamic event-triggered scheduling was proposed to deal with the problem of limited bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Average-ILC-Based Consensus Tracking of Multiagent Systems over Wireless Networks in Presence of Channel Noise and Data Dropout

Fang

Sheng

2021

Mathematical Problems in Engineering

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In a multiagent system (MAS), communication signals are affected by harsh wireless networks when they are transmitted from an agent to its neighboring agents, leading to the inconsistency of the MAS. In this paper, an average-iterative learning control (average-ILC) method is studied to address the consensus problem of MAS over wireless networks in the presence of channel noise and data dropout. The combined effects of channel noise and data dropout on iterative learning controllers are carefully analyzed. Based on graph theory and mathematical expectation, the corresponding average-iterative learning scheme is proposed. Especially, a sufficient condition is derived for the average-iterative learning scheme. Rigorous theoretical analysis demonstrates that the convergence of the covariance matrix of tracking error can be guaranteed with the help of an average-iterative learning scheme. Finally, simulation results are given to show the effectiveness of the proposed method.

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“…The effectiveness of the proposed method is verified by two examples. Our future work will consider some network-induced complexities such as network-induced time-delays [42] and security-related issues [43].…”

Section: Examplesmentioning

confidence: 99%

Aperiodic Sampled-Data H_∞Filtering for Lipschitz Nonlinear Systems: An Impulsive System Approach

Zhu

et al. 2021

IEEE Access

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This paper is concerned with the H ∞ filtering for Lipschitz nonlinear systems under aperiodically sampled measurements. The developed filter is a hybrid system, whose states undergo a change or reset at sampling instants. The resulting filtering error system is then modelled as a kind of nonlinear impulsive systems. By introducing a time-varying Lyapunov functional candidate, a sufficient condition for the existence of desired filter is derived to ensure the filtering error system asymptotic stability and guarantee an H ∞ performance. The optimal H ∞ performance and corresponding filter gain matrix can be obtained by solving a convex problem with linear matrix inequalities (LMIs) constrains. Two examples are given to show the effectiveness of the theoretical results, and our results are less conservative than existing ones through comparisons.INDEX TERMS Lipschitz nonlinear systems, Sampled-data filtering, H ∞ performance, Impulsive system approach.

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Recursive filtering of networked nonlinear systems: a survey

Cited by 197 publications

References 111 publications

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

Average-ILC-Based Consensus Tracking of Multiagent Systems over Wireless Networks in Presence of Channel Noise and Data Dropout

Aperiodic Sampled-Data H_∞Filtering for Lipschitz Nonlinear Systems: An Impulsive System Approach

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Recursive filtering of networked nonlinear systems: a survey

Cited by 197 publications

References 111 publications

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

Adaptive Event‐Triggered Synchronization of Networked Neural Networks with Time‐Varying Delay Subject to Actuator Saturation

Average-ILC-Based Consensus Tracking of Multiagent Systems over Wireless Networks in Presence of Channel Noise and Data Dropout

Aperiodic Sampled-Data H∞Filtering for Lipschitz Nonlinear Systems: An Impulsive System Approach

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Aperiodic Sampled-Data H_∞Filtering for Lipschitz Nonlinear Systems: An Impulsive System Approach