# Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data
## 1 Introduction  
USVs are highly regarded in the field of ocean engineering and have attracted attention from various disciplines, including control systems and ship engineering [1; 2; 3; 4]. In practice, the practical tasks of USVs include path following, tracking control, stability control, dynamic positioning, and CKC [5; 6]. For these control tasks, the issue of CKC is one of the most common control problems, which is to make USVs sail along a given trajectory by controlling the steering equipment [7; 8; 9]. For the issue of CKC, many effective design approaches have been reported, such as active disturbance rejection control (ADRC) [10], PID [11], backstepping [12], sliding mode control (SMC) [13], and Ho [14]. Furthermore, intelligent ADRC [15], fuzzy PID [16; 17], neural PID [18], adaptive backstepping [19], and non-fragile Ho [20] are proposed to resolve the issue of CKC for USVs. Under these control schemes, the desired control performance was obtained. However, these control schemes are based on the assumption that the course and yaw must be continuously available. Unfortunately, the navigation control of USVs is carried out under a network environment, which implies that the continuous signals required by the navigation control cannot be obtained. For such a case, under a network environment, the superior control schemes mentioned above cannot be transplanted to resolve such CKC design issues of USVs since the signals cannot be guaranteed to be transmitted continuously. Under a network environment, the CKC system of USVs is a typical cyber-physical system. In this case, several challenging problems should be taken into account in the control design, for example, the network resource constraint problem caused by the network bandwidth, the discontinuity of course and yaw caused by the data transmissionprocess, and so on. In the existing works, event-triggering control (ETC) [21; 22] can reduce the data transmission such that the constrained issue of network resource can be relaxed. Unlike the traditional time-triggered control methods, in the ETC-based method, an ETM is constructed in the controller-actuator (C-A) channel. Thus, the control commands are transmitted as long as the preset triggering condition is satisfied. In this context, several ETC-based schemes were reported for the control issue of USVs, like course tracking control [23], tracking control [24; 25], and stable control [26]. However, the event-triggering mechanism (ETM) in references [23; 24; 25; 26] is only established between the controller and the actuator, which implies that the transmission of course data cannot be resolved. To further save the network resources, it is essential that a minimum amount of data is transmitted, i.e., in reducing the transmission of control commands and course data at the same time. Obviously, there is a need to explore a new method to solve the problem of limited network resources. Based on the above observation, this work discusses an event-sampled adaptive neural CKC problem for USVs. An ETM is established in the sensor-controller (S-C) channel, and an event-sampled mechanism (ESM) is developed to obtain the course data, such that only the intermittent course data are transmitted in the control design. To resolve the control design issue caused by the absence of yaw, a novel event-sampled adaptive NN-SO is developed to achieve the refactoring of yaw. In addition, to save the network resource, a ETM is established in the controller-actuator (C-A) channel. Finally, a dual-channel ETANOFC solution is proposed. In comparison with the existing results, the main contributions of this work can be listed as follows:  
* A dual-channel (S-C and C-A channels) ETANOFC solution is developed. Compared with the existing works [23; 24; 25; 26], the developed control solution only needs the intermittent output signal, i.e., intermittent course data.