Model-free adaptive obstacle avoidance control for multiple unmanned surface vehicles

This paper presents a model-free adaptive obstacle avoidance control (MFAOAC) algorithm based on compact form dynamic linearization (CFDL) technology and an improved artificial potential field (IAPF) method for multiple unmanned surface vehicles (USVs) with unknown disturbance term. The CFDL technique is employed to transform the complex nonlinear USV system into a CFDL data model with an unknown disturbance term. An extended state observer (ESO) is utilized to estimate and compensate for this disturbance. The artificial potential field method is enhanced by introducing a velocity ratio and a tangential force to achieve flexible obstacle avoidance and collision avoidance in unknown environments. Finally, the effectiveness of the proposed control algorithm is verified through simulation involving four USVs.