Consensus Control of A Class of Second-order Nonlinear Multi-agent Systems Based on Distributed Adaptive Protocol

Zhang, Anguo; Zhang, Shuxun; Chen, Qing; Zhu, Wei

doi:10.1109/ccdc.2018.8407938

Cited by 3 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, most of existing works only focus on firstor second-order dynamic systems, [13][14][15][16][17][18][19][20] while many practical engineering systems are high-order dynamics based, such as single-link flexible-joint manipulators, 21,22 robot formation cooperation, 23,24 and synchronous generator coordination. [25][26][27] On the other hand, due to the model uncertainty, nonlinear dynamics, as well as the system noise, makes it difficult even impossible to obtain the accurate control model in mathematical.…”

Section: Introductionmentioning

confidence: 99%

Neuro-adaptive cooperative control for a class of high-order nonlinear multi-agent systems

Li¹,

Zhang

Cheng³

2022

Measurement and Control

View full text Add to dashboard Cite

In this paper, we studied the cooperative control problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty. A neuro-adaptive robust controller with sliding mode variable structure method, with an online-learning RBF-like neural network was proposed to approximate the nonlinear terms. Further, sliding mode variable structure method was used to eliminate the influence of external disturbance and system uncertainty. Lyapunov stability theorem verified the capability of system consensus, and the sufficient conditions for cooperatively uniformly ultimately bounded (CUUB) are also given. At last, two numerical simulations on both homogeneous and heterogeneous MASs demonstrated the effectiveness of our proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Neuro-adaptive cooperative control for a class of high-order nonlinear multi-agent systems

Li¹,

Zhang

Cheng³

2022

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…First, existing research on MASs mostly focuses on first-order and second-order systems (Zhang et al, 2018;Wu et al, 2018;Li et al, 2018;Duan et al, 2020;Zou et al, 2020;Wang et al, 2020;Yao et al, 2020;Fu et al, 2020). However, in engineering practice, single-link flexible-joint manipulators (Zhang, 2008;Ling et al, 2019), robot formation cooperation (Miao et al, 2018;Zong et al, 2019), and synchronous generator coordination (Fathi et al, 2018;Abdelrahem et al, 2018;Nian and Jiao, 2020) are based on high-order dynamic modeling.…”

Section: Introductionmentioning

confidence: 99%

Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties

Cheng

Zhang

2021

View full text Add to dashboard Cite

The consensus problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty is studied. We design an online-update radial basis function (RBF) neural network based distributed adaptive control protocol, where the sliding model control method is also applied to eliminate the influence of the external disturbance and system uncertainty. System consensus is verified by using the Lyapunov stability theorem, and sufficient conditions for cooperative uniform ultimately boundedness (CUUB) are also derived. Two simulation examples demonstrate the effectiveness of the proposed method for both homogeneous and heterogeneous MASs.

show abstract

“…The difficulty of consensus control for nonlinear systems owes to certain restrictions the nonlinearity imposes when using the information of the individual systems. Consensus control for second-order Lipschitz nonlinear multi-agent systems was addressed in [12]- [14]. The consensus problem of high-order multiagent systems with nonlinear dynamics was studied in [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Consensus Control for a Network of Multiple Elastic Joints Robots with Algebraic Riccati Equation (Are)

2019

IJRTE

View full text Add to dashboard Cite

This paper deals with the leader-follower consensus problem of Lipschitz nonlinear systems under fixed directed communication networks specifically for multiple Elastic Joints Robots. A State-feedback consensus controller is proposed for the system, based on state and output measurements of neighbouring agents, respectively. The controller gain is obtained with Algebraic Riccati Equation (ARE) protocol. An example is included to demonstrate the effectiveness of the state consensus controller. It can be shown that both controllers are stable and able to reach consensus for the system despite the influence of the nonlinear Lipschitz element from the robot.

show abstract

Consensus Control of A Class of Second-order Nonlinear Multi-agent Systems Based on Distributed Adaptive Protocol

Cited by 3 publications

References 22 publications

Neuro-adaptive cooperative control for a class of high-order nonlinear multi-agent systems

Neuro-adaptive cooperative control for a class of high-order nonlinear multi-agent systems

Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties

Consensus Control for a Network of Multiple Elastic Joints Robots with Algebraic Riccati Equation (Are)

Contact Info

Product

Resources

About