Neural-network-based integral sliding-mode tracking control of second-order multi-agent systems with unmatched disturbances and completely unknown dynamics

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.