Regular Papers

International Journal of Control, Automation and Systems 2017; 15(4): 1925-1935

Published online July 20, 2017

https://doi.org/10.1007/s12555-016-0057-z

© The International Journal of Control, Automation, and Systems

Neural-Network-Based integral sliding-mode tracking control of secondorder multi-agent systems with unmatched disturbances and completely unknown dynamics

Xi Ma*, Fuchun Sun, HongBo Li, and Bing He

Tsinghua University

Abstract

This paper investigates the tracking control problem of second-order multi-agent systems (MASs) in the presence of unmatched disturbances and completely unknown dynamics. The extended state observer (ESO) and neural networks (NNs) are utilized to estimate and compensated the unmatched disturbances and unknown dynamics, respectively. By constructed a novel integral sliding-mode manifold incorporated with ESO output, a neural-network-based control algorithm is developed. Meanwhile, by Lyapunov theoretical analysis, the UUB stability of the tracking errors as well as within a sufficiently small region is guaranteed by the appropriate choice of the parameters. Simulation results show that the proposed method exhibits much better control performances than the traditional I-SMC method, such as great robustness, reduced chattering and more accurate."

Keywords Completely unknown dynamics, extend state observer, integral sliding-mode control, neural networks, second-order multi-agent systems, unmatched disturbances.

Article

Regular Papers

International Journal of Control, Automation and Systems 2017; 15(4): 1925-1935

Published online August 1, 2017 https://doi.org/10.1007/s12555-016-0057-z

Copyright © The International Journal of Control, Automation, and Systems.

Neural-Network-Based integral sliding-mode tracking control of secondorder multi-agent systems with unmatched disturbances and completely unknown dynamics

Xi Ma*, Fuchun Sun, HongBo Li, and Bing He

Tsinghua University

Abstract

This paper investigates the tracking control problem of second-order multi-agent systems (MASs) in the presence of unmatched disturbances and completely unknown dynamics. The extended state observer (ESO) and neural networks (NNs) are utilized to estimate and compensated the unmatched disturbances and unknown dynamics, respectively. By constructed a novel integral sliding-mode manifold incorporated with ESO output, a neural-network-based control algorithm is developed. Meanwhile, by Lyapunov theoretical analysis, the UUB stability of the tracking errors as well as within a sufficiently small region is guaranteed by the appropriate choice of the parameters. Simulation results show that the proposed method exhibits much better control performances than the traditional I-SMC method, such as great robustness, reduced chattering and more accurate."

Keywords: Completely unknown dynamics, extend state observer, integral sliding-mode control, neural networks, second-order multi-agent systems, unmatched disturbances.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

Stats or Metrics

Share this article on

  • line

Related articles in IJCAS

IJCAS

eISSN 2005-4092
pISSN 1598-6446