## 1 Introduction
In [26], the proposed controller was robust to the parameter uncertainties of the nonlinear terms and exogenous disturbances. Unknown plant parameters and environmental disturbances were solved using parameter estimation and upper-bound estimation in [27]. In [28], a robust adaptive control algorithm was proposed to solve uncertain parameters. Adaptive estimators were designed to estimate unknown time-varying functions of vessel model in [29]. Considering the leader-follower formation tracking problem, a neural network was used to approximate the uncertainties, and the uncertainties were compensated by online learning [30]. A cooperative controller was proposed using position-heading measurements in [31], and an extended-state observer (ESO) was constructed to provide the estimations of velocity, yaw rate and uncertainties. However, the existing observers presented in [14; 16; 19; 25; 31; 32] relied on continuous sampling. In each execution cycle, signals are transmitted, which means that many network resources and calculation resources are consumed. To avoid unnecessary calculation and communication, an event-triggered extended-state observer (ETESO) was proposed in [33]. In [34], an ETESO was proposed for a class of non-linear networked control systems, and the state estimation problem was solved. As for USVs, when the working mode is tracking control, in most cases, the yaw angle is unchanged. In the common continuous observer, the signals have to be transmitted during each execution cycle, consuming many network resources and calculation resources. Therefore, it is rewarding to design a leader-follower tracking controller with a light transmission load and short action times of actuators. Based on the above research background, in this paper, we investigate leader-follower formation tracking control of USVs subject to input saturation, model uncertainties and environmental disturbances. Inspired by the work reported in [23; 31], an improved output feedback controller was proposed. First, an ETESO was used to recover the velocity data and model uncertainties and disturbances. In each execution cycle, an event-triggered strategy is monitored to determine whether or not to transmit the position and head information to the observer. Sensor-to-observer communication costs are drastically reduced. Secondly, an estimator is employed to estimate the velocity of the leader and reduce the amount of data exchanged in formation. Thirdly, auxiliary variables provide a solution for the problems of input saturation and underactuation. Then, an event-triggered controller (ETC) was designed based on the ETESO, the estimator and auxiliary variables. Finally,the stability of the closed-loop system was proven. Simulations were also conducted to demonstrate the proposed control strategy. Two salient features of the proposed control are summarized as follows. First, an estimator is employed to estimate the velocity of the leader, the embedded processor of each follower performs reading computations only using the position and yaw of the leader and the transmission loads between formations are saved. Second, an event-triggered mechanism is designed in the controller-to-actuator channel, and action times of actuator are reduced. Compared with the existing related results, the novelty of this paper is summarized as follows. First, in contrast to existing formation control approaches [25; 31], the velocity of the leader is estimated by an estimator, which reduces the amount of data exchanged between USVs. Second, compared with the observer proposed in [31], the ETESO can reduce sensor-to-observer communication costs. Third, compared with the strategy proposed in [23], the proposed control law can largely reduce the action times of the actuator. The remainder of this paper is organized as follows. A list of notations defining variables used in this paper is presented in Table 1. Section 2 describes the preliminaries and problem formulation.