Quantized-communication-based neural network control for formation tracking of networked multiple unmanned surface vehicles without velocity information

Abstract

This paper proposes a quantized communication-based output-feedback control strategy for formation tracking of networked unmanned surface vehicles (USVs) with uncertainty. Under limited network communication, it is assumed that each USV measures only the position and orientation information. In particular, this information is quantized and transmitted to USVs connected to a band-limited directed network. The primary contributions of this study are to derive distributed learning laws for neural networks using discontinuous signals and to analyze the stability of the neural network-based output-feedback control system designed in a quantized communication environment. A neural network-based local observer is developed to estimate the velocity information of each USV with model uncertainty and external disturbance. Then, a neural network-based output-feedback control design strategy using distributed and quantized postures is presented to accomplish the desired formation of networked USVs with uncertainty and underactuation. The distributed learning laws of neural networks are derived using neighbors’ quantized signals. The auxiliary signal and approach angle are employed to solve the underactuation and stability analysis problems. Despite the discontinuity of quantized signals, it is proven that all errors in the closed-loop system are bounded and can be made arbitrarily small. Finally, simulation results are given to verify the theoretical results of the proposed control system.