Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Thang Nguyen Trong and Minh Nguyen Duc
Additional contact information
Thang Nguyen Trong: Department of Electrical Engineering and Automation, Haiphong Private University, Haiphong 181810, Vietnam
Minh Nguyen Duc: Department of Electrical Engineering and Automation, Haiphong Private University, Haiphong 181810, Vietnam

Energies, 2017, vol. 10, issue 12, 1-15

Abstract: Based on Lyapunov theory, this research demonstrates the stability of the sliding surface in the consensus problem of multi-agent systems. Each agent in this system is represented by the dynamically uncertain robot, unstructured disturbances, and nonlinear friction, especially when the dynamic function of agent is unknown. All system states use neural network online weight tuning algorithms to compensate for the disturbance and uncertainty. Each agent in the system has a different position, and their trajectory approach to the same target is from each distinct orientation. In this research, we analyze the design of the sliding surface for this model and demonstrate which type of sliding surface is the best for the consensus problem. Lastly, simulation results are presented to certify the correctness and the effectiveness of the proposed control method.

Keywords: Euler–Lagrange System; neural network; consensus; sliding mode control; multi-agent system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/12/2127/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/12/2127/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:12:p:2127-:d:122917

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().