Objective To address the problem of inadequate path-following accuracy and stability for unmanned surface vehicles (USVs) under complex environments (characterized by uncertainties such as varying wind speeds and initial position deviations), a guidance method named Time-varying Sideslip angle Compensated Adaptive Line-of-Sight (TSC-ALOS) is proposed.
Method Firstly, a time-varying sideslip angle compensation mechanism based on real-time wind speed and direction measurement data is introduced, leading to the development of the improved TSC-ALOS algorithm. This mechanism dynamically compensates for sideslip angle variations induced by environmental disturbances, thereby optimizing the desired heading output for the USV. Subsequently, a Proportional-Derivative (PD)-based heading controller is designed. This controller converts the desired heading generated by the TSC-ALOS algorithm into actual rudder angle commands, ensuring the USV can rapidly and stably track the target heading and achieving effective linkage from high-level navigation strategy to low-level control execution. Finally, numerical simulations in real marine environments are conducted. The performance of TSC-ALOS, Adaptive LOS (ALOS), and traditional LOS algorithms is compared under three operational conditions: no wind, fixed wind, and random wind. Key metrics such as cross-track error and heading stability are specifically analyzed.
Results Simulation results demonstrate that in windless environments, both TSC-ALOS and ALOS algorithms exhibit superior path-following accuracy compared to traditional LOS, particularly excelling at path turns. Under fixed wind (wind speed: 8.37 m/s) and random wind (wind speed: 16.73 m/s) conditions, TSC-ALOS significantly reduces the cross-track error, showcasing stronger anti-disturbance capability. In scenarios with initial position deviations, the average cross-track error of TSC-ALOS is reduced by 24.6% and 36.8% compared to ALOS and LOS algorithms, respectively.
Conclusion The TSC-ALOS algorithm demonstrates superior guidance performance across various complex environments, with particularly notable advantages in handling environmental interference and position deviations. It provides crucial technical support for the development of autonomous navigation systems for USVs and suggests research directions for further algorithm optimization.
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