## 1 Introduction  
In the last few years, the unmanned surface vehicle (USV) as an intelligent offshore platform facility has appealed to researchers from many fields [1; 2; 3; 4] because of its small volume and fast speed. It is widely used in chaotic marine environments, especially when manual intervention is impossible. The path following control of the USV drives the vehicle to accurately track the expected route without strict time constraints [5; 6; 7]. Nowadays, line-of-sight (LOS) guidance, which provides path information for a path following controller, is a main research direction of USV path following control as a result of its high efficiency and simplicity. Paper [8] verifies that the proportional LOS guidance method is simple and practical. Papers [9; 10; 11; 12] improve the LOS guidance method by introducing the integral term, which effectively improves the influence under the condition of constant flow. For the sake of handling the issue of underactuated vehicle path following with uncertainty and input saturation, an improved LOS (ILOS) guidance method is proposed in Paper [13]. A predictor-based LOS (PLOS) guidance method, which effectively reduces the influence of sideslip angle and obtains a more accurate guidance control law, is proposed in Paper [14]. Based on an extended state observer (ESO), Paper [15] proposes an ESO LOS (ELOS) guidance method to handle the issue of time-varying sideslip angle and to improve the path following effect of underactuated unmanned vehicles when drift angle changes. Paper [16] puts forward an IALOS guidance law to effectively improve the path following effect of USVs when sideslip angle and ocean current are time-varying. Paper [17] proposes ocean current observer LOS guidance, which can more quickly compensate the impact of ocean current on path following. Paper [18] proposes a compound LOS (CLOS) guidance law to solve the problem of excessive sideslip angle. For the sake of overcoming the issue of time delay caused by stochastic differential rules, a global robust adaptive controller is designed in Paper [19] by drawing into weak and strong nonlinear Lyapunov functions. Sliding mode control (SMC) is extensively utilized in the field of vehicle control on account of its strong robustness and fast response. The integral sliding mode control method is applied to unmanned surface vehicles for the first time in Paper [20]. Paper [21] designs a fast nonsingular terminal SMC, which has the advantage of rapidity and robustness. Papers [22; 23] design auxiliary dynamic systems to approach the actuator saturation issue in the course of vehicle motion in practice. In [24], based on the finite time theory, an external time-varying disturbance is quickly approached in a finite time by putting forward a finite-time disturbance observer (FCDO). Paper [25], based on minimal learning parameter (MLP) technology and neural network technology, proposes a neural network disturbance observer to effectively solve the observation of disturbance when the vehicle model is uncertain. Paper [26] proposes a robust adaptive neural track tracking control strategy by combining dynamic surface control (DSC) with the MLP method. The designed adaptive law replaces the estimation of neural network weight itself by estimating the norm of neural network weight online, which effectively solves the problem of excessive computation caused by adaptive neural networks in Paper [27; 28]. For the sake of overcoming the issue that the parameter learning time of neural network technology is too long, resulting in poor real-time performance and difficult implementation in practical engineering, a single-parameter neural network is proposed in Paper [29]. Papers [30; 31; 32; 33; 34] employ model-predictive-control methods, efficiently settling the issue of model uncertainty. Enlightened by the aforesaid observations, IALOS guidance is introduced to supply guidance.