Neural network sliding mode control of multi-agent systems based on a preview mechanism and whale optimization algorithm

In this work, a neural network sliding mode control (SMC) scheme is proposed to address the tracking issue of discrete-time multi-agent systems (MASs) with unknown nonlinearities by combining the preview mechanism and whale optimization algorithm. An augmented error system (AES) was constructed, which includes previewable reference and disturbance signals. A new sliding mode surface is designed for AES, and the stability criteria are proposed for the sliding mode dynamics. Utilizing the preview mechanism and whale optimization algorithm, the neural network-based SMC law is designed to satisfy the discrete-time reachability condition. Two simulation examples are provided to demonstrate that the proposed control scheme can effectively enhance the tracking performance of MASs.