Coordinated Tracking for Nonlinear Multiagent Systems under Variable‐Time Impulsive Control

Tian, Yuan; Li, Chuandong; Yang, Xujun; Han, Yiyan

doi:10.1155/2019/5371573

Cited by 2 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By simple computation, one gets l f = 0.5, l τ = 0.1π, θ = 0.3, ν = 0.1, I 2×3 + BL ⊗ I 2 = 0.7 < 1, therefore, (A1) holds. From (12), it has HG(x,t) ≤ 2l f H δ (t) , so Thus, condition (A2) holds. Since A1 and A2 hold, Theorems 1-6 are valid for the example system, system (20) and its corresponding comparison system have the same stability.…”

Section: Numerical Experimentsmentioning

confidence: 94%

“…This is evidenced by the widespread applications to different fields, such as: e-commerce, healthcare, decision support, sensor networks and control systems [1][2][3][4][5]. The research directions include different dynamical agents, different networks topologies and so on [6][7][8][9][10][11][12][13]. As for the direction of agent dynamic, the dynamics are mathematically modeled as simple linear functions in many works, which limit the performance of agents.…”

Section: Introductionmentioning

confidence: 99%

“…As for the direction of agent dynamic, the dynamics are mathematically modeled as simple linear functions in many works, which limit the performance of agents. Thus, nonlinear MASs have developed from researchers in recent years, and convenient for discussion, most works model nonlinear dynamics satisfying strict conditions (Lipschitz condition, up-bounded and so on) [10][11][12][13]. In fact, neural networks as typical nonlinear systems have well performance in intelligent fields, especially, Hopfield-type neural networks(HNNs) have successfully used in signal processing, associative memories, optimization problems and so on [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

State-dependent Impulsive Control for Consensus of Multi-agent Systems

Tian

2021

Int. J. Control Autom. Syst.

Self Cite

View full text Add to dashboard Cite

This paper aims to investigate the consensus of multi-agent systems, where each agent runs hopfied-type neural networks, by means of the state-dependent impulsive control. The challenge comes from the fact that the occurrence of impulse varies according to the state of the agent, which means it is hard to predict when an impulse occurs. To solve this problem, we try to transform the impulsive system from state-dependent to fixed-time. In order to do that, we first construct a global consensus error state-dependent with a multi-agent system according to the impulsive consensus protocol. Then we use the B-equivalence method to form a comparison impulsive system of fixed-time impulsive sequence. Using Lyapunov stability theory, we prove that these two systems have the same stability. So we establish sufficient consensus conditions of multi-agent system by analyzing the comparison system. We perform extensive evaluations to validate the correctness of theoretical results and the effectiveness of the statedependent impulsive consensus protocol.

show abstract

Section: Numerical Experimentsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State-dependent Impulsive Control for Consensus of Multi-agent Systems

Tian

2021

Int. J. Control Autom. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

State of health prediction for lithium‐ion batteries with a novel online sequential extreme learning machine method

Tian

Qin

2020

Int J Energy Res

View full text Add to dashboard Cite

Summary State of health (SOH) prediction is always a research hotspot in the field of lithium‐ion batteries (LIBs). Machine learning (ML) methods have received widespread attention for their high prediction accuracy. However, the existing studies only focus on extracting features from simple constant current charge‐discharge curves or using features that require pre‐processing, while the actual discharge current is random and can affect battery aging. Besides, the online sequential extreme learning machine (OSELM) currently used in ML lacks a more efficient online learning and update mechanism in terms of prediction. Therefore, this paper firstly extracts effective features from the random discharge data and conducts a mechanism analysis to verify its rationality. Then, we propose a drift detection based on the Bernstein inequality (BI‐DD) algorithm and use it to guide the OSELM to save learning time. The experimental results show the OSELM based on the BI‐DD can perform good learning for SOH prediction in a shorter time. The learning time can be reduced by up to 88.87% and the mean absolute error (MAE) does not exceed 1%, which is a promising SOH prediction method. Highlights Extract aging features from random discharge data and the rationality of the extracted features is analyzed according to the mechanism. A drift detection algorithm based on Bernstein inequality (BI‐DD) is proposed. An OSELM based on concept drift detection is proposed and for SOH online learning and prediction.

show abstract

Coordinated Tracking for Nonlinear Multiagent Systems under Variable‐Time Impulsive Control

Cited by 2 publications

References 21 publications

State-dependent Impulsive Control for Consensus of Multi-agent Systems

State-dependent Impulsive Control for Consensus of Multi-agent Systems

State of health prediction for lithium‐ion batteries with a novel online sequential extreme learning machine method

Contact Info

Product

Resources

About