Fixed-time Group Consensus of Nonlinear Multi-agent Systems via Pinning Control

Hao, Lili; Zhan, Xisheng; Wu, Jie; Han, Tao; Yan, Huaicheng

doi:10.1007/s12555-019-1005-5

Cited by 59 publications

(28 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed algorithms in this article are based on this identification model. Many identification methods are derived based on the identification models of the systems [32][33][34][35] and can be used to estimate the parameters of other linear systems and nonlinear systems [36][37][38][39] and can be applied to other fields [40][41][42][43] such as chemical process control systems.…”

Section: Problem Statementmentioning

confidence: 99%

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Cui

Ding

et al. 2020

Adaptive Control & Signal

View full text Add to dashboard Cite

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.

show abstract

Section: Problem Statementmentioning

confidence: 99%

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Cui

Ding

et al. 2020

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…The proposed algorithms in this article are based on the identification models. Many identification methods are derived based on the identification models of the systems and can be used to estimate the parameters of other linear systems and nonlinear systems [37][38][39][40][41][42][43] and can be applied to fields [44][45][46][47][48] such as chemical process control systems.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Replacing (16) and (19) in the F-MI-GSG algorithm with (42) and (43), we get the F-MI-GFG algorithm for the RBF-ARAR model.…”

Section: The F-mi-gfg Algorithmmentioning

confidence: 99%

Parameter identification of a nonlinear radial basis function‐based state‐dependent autoregressive network with autoregressive noise via the filtering technique and the multiinnovation theory

Zhou

Ding

et al. 2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article studies the parameter estimation problems of radial basis function-based state-dependent autoregressive models with autoregressive noises (RBF-ARAR models). To reduce the effect of the colored noise to parameter estimation, the data filtering technique is applied and a filtering based generalized stochastic gradient algorithm is derived for the RBF-ARAR models. In order to achieve more accurate parameter estimates, a filtering based multiinnovation generalized stochastic gradient (F-MI-GSG) algorithm is proposed by utilizing the current and past innovations. Introducing two forgetting factors, a filtering based multiinnovation generalized forgetting gradient algorithm is developed to improve the transient performance of the F-MI-GSG algorithm. The effectiveness of the proposed algorithms is verified through the simulation examples.

show abstract

“…n 1 and n 2 represent the additive white Gaussian noise in the feedback and the forward channels, respectively. ϕ 2 2 and ϕ 2 3 denote the power spectral densities of n 1 and n 2 , respectively. The reference signal r is considered a random signal, and the variance of the random process is ϕ 2 1 .…”

Section: Problem Statementmentioning

confidence: 99%

“…ϕ 2 2 and ϕ 2 3 denote the power spectral densities of n 1 and n 2 , respectively. The reference signal r is considered a random signal, and the variance of the random process is ϕ 2 1 . According to Fig.1, following can be obtained:…”

Section: Problem Statementmentioning

confidence: 99%

Performance Analysis of Networked Systems With Two-Channel Noise and Bandwidth Constraints

Yang

Zhan

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

In this paper, the performance of networked systems with two-channel noise and bandwidth constraints is investigated. The given plant with non-minimum phase zero and unstable pole are considered in the system. Considering the white noise constraint in two channels (forward and feedback channels) and the bandwidth constraint in the feedback channel, the expression of performance limitations is obtained by using spectral decomposition technique and selecting the optimal single parameter. The obtained results demonstrate that the performance of networked systems depends on the intrinsic properties of the given plant (such as non-minimum phase zeros and unstable poles), and the network parameters (such as white noise, bandwidth and codec). Furthermore, the results also show the impacts of two-channel noise and the bandwidth constraints on the performance of networked systems. Finally, a classical example is presented to illustrate the theoretical results. INDEX TERMS networked systems, performance limitation, white noise, bandwidth constraint.

show abstract

Fixed-time Group Consensus of Nonlinear Multi-agent Systems via Pinning Control

Cited by 59 publications

References 54 publications

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Parameter identification of a nonlinear radial basis function‐based state‐dependent autoregressive network with autoregressive noise via the filtering technique and the multiinnovation theory

Performance Analysis of Networked Systems With Two-Channel Noise and Bandwidth Constraints

Contact Info

Product

Resources

About