Hybrid control scheme of nonlinear model prediction and adaptive terminal sliding mode for underwater vehicles based on threshold switching.

This paper addresses the challenge of balancing real-time performance and control smoothness in trajectory tracking for underwater vehicles. To this end, a novel hybrid control scheme that switches between two strategies based on a tracking error threshold is proposed. For large errors, a feedback-linearized model predictive control (FL-MPC) algorithm ensures stability and smooth thruster response with reduced computational burden. For precise tracking, a terminal sliding mode control method enhanced by dual radial basis function neural networks (DRBF-TSMC) is developed to compensate for lumped uncertainties. Simulations and hardware-in-the-loop experiments demonstrate that the proposed control scheme achieves comparable or superior tracking accuracy, while markedly outperforming benchmark controllers in real-time performance and thrust smoothness.