Dynamic STA Quantized trajectory tracking control for unmanned surface vehicles without velocity measurements

Objectives To address the trajectory tracking and intermittent communication problem for the underactuated unmanned surface vehicle under the external disturbances and limited transmission resources, this paper proposes an improved dynamic super-twisting-algorithm (IDSTA) based quantized control strategy without velocity measurements. Methods Firstly, considering no relative degree in the input-output dimension for underactuated USV, a virtual input-based dynamic inversion method is introduced to achieve a relative degree. Then, a second-order observer is designed to accurately estimate its trajectory and velocity due to the unavailable velocity measurements. Besides, a radial basis function neural network (RBFNN) is employed online approximate unknown nonlinearities. Building upon these components, an improved dynamic STA controller is established to mitigate the chattering and improve the system robustness which incorporates input quantization effects into the control design. Results The simulation results demonstrate that the velocity observer can estimate the actual velocity within the finite time. And the proposed strategy achieves high-precision trajectory tracking and maintains excellent transient and steady-state performance in complex marine environments. Specifically, the position errors can converge within 0.2 meters and the yaw errors are restricted in 0.01 rad whereas the communication frequency in the surge and yaw channels can be reduced by 89% and 72.9%, respectively. Conclusions The proposed control algorithm can provide the accurate tracking performance for underactuated USV by utilizing velocity observer, RBFNN technique and improved dynamic STA. It offers a practical and reliable solution for autonomous USV control with notable theoretical and engineering value.