State constrained control strategy for unmanned surface vehicle trajectory tracking based on improved barrier Lyapunov function

This paper addresses the state-constrained control of the symmetric underactuated unmanned surface vehicles (USVs) under unknown dynamics and time-varying interference based on improved Lyapunov function. Inspired by the prescribed performance control (PPC), the improved logarithmic barrier Lyapunov function (BLF) is proposed with a reasonably defined performance function in advance. The tracking errors of position and yaw direction are constrained within the set range. Meanwhile, the symmetric barrier Lyapunov function (SBLF) and dynamic surface control (DSC) are introduced to simplify the controller design and indirectly restrict the yaw velocity. On this basis, the finite-time disturbance observer (FDO) is proposed to estimate the unknown disturbance. The finite-time auxiliary dynamic system is introduced to deal with the input saturation. In addition, the neural network minimum learning parameter (MLP) can reduce the computational complexity while figuring out the unknown dynamics. Finally, numerical simulation verifies the effectiveness of the control system.

Automated summary

This paper proposes a method for state-constrained control of symmetric underactuated unmanned surface vehicles (USVs) under unknown dynamics and time-varying interference based on an improved Lyapunov function. The improved logarithmic barrier Lyapunov function (BLF) is introduced, with a reasonably defined performance function, to constrain the tracking errors of position and yaw direction. The symmetric barrier Lyapunov function (SBLF) and dynamic surface control (DSC) are used to simplify the controller design and indirectly restrict the yaw velocity. The effectiveness of the control system is verified through numerical simulation.