Model free adaptive control of nonlinear second-order multi-agent systems based on backstepping under mixed attacks

Dong Liu

Abstract:

This paper employs model-free adaptive control
(MFAC) methods to investigate the trajectory tracking control
problem of second-order nonlinear multi-agent systems (MASs)
under mixed attacks. Initially, since mixed network attacks
significantly impact system stability, predictive-based compensation
mechanisms are designed to reduce the effects of these
attacks. Furthermore, based on the backstepping method and
MFAC techniques, virtual desired velocities are constructed to
decompose the second-order multi-agent systems into two interconnected
subsystems. Moreover, leveraging the desired states of
each subsystem, distributed MFAC schemes are proposed using
the backstepping approach to achieve trajectory tracking control
of the second-order multi-agent systems. Finally, the effectiveness
of the proposed method is validated through two simulation
examples, demonstrating robustness and efficiency in countering
the adverse impacts of mixed attacks.

Submission Number: 25