Model-free finite-time formation containment control of underactuated UUVs subject to input delays and unknown interaction information

This paper put forwards a model-free adaptive neural finite-time formation-containment (FC) control scheme for time-delayed underactuated unmanned underwater vehicles (UUVs) with unknown dynamic models and some interaction information under switching topologies. The UUVs are steered by multiple child leaders moving along with a root leader, where all leaders are virtual and defined by a new generalized dynamic. A novel autoregressive model-based state predictor is created for each UUV, which can construct future motion states online relying on current and stored states. Considering the containment layer, a finite-time super-twisting observer is used to recover the unavailable interaction information. Moreover, observer-based finite-time kinematic guidance laws employing only 3D positions of the neighboring UUVs are devised, together with protocols for completing formation in finite time. Aiming to accurately identify unknown dynamics and control gains simultaneously, estimator-based adaptive neural networks are introduced exploiting both historical and real-time data. Finally, model-free anti-disturbance finite-time kinetic control protocols are designed based on the data-driven adaptive neural networks and nonlinear tracking differentiators. It is proven that all signals of the closed-loop FC system are bounded, the deployed FC tracking can be achieved in finite time. Simulation results verify the feasibility and superiority of the proposed FC control scheme.