The feedback PID method was mainly used for the navigating control of an unmanned surface vessel (USV). However, when the\nintelligent control era is coming now, the USV can be navigated more effectively. According to the USV character in its navigating\ncontrol, this paper presents a parallel action-network ADHDP method. This method connects an adaptive controller parallel to the\naction network of the ADHDP. The adaptive controller adopts a RBF neural network approximation based on the Lyapunov\nstability analysis to ensure the system stability. The simulation results show that the parallel action-network ADHDP method has\nan adaptive control character and can navigate the USV more accurately and rapidly. In addition, this method can also eliminate\nthe overshoot of the ADHDP controller when navigating the USV in various situations. Therefore, the adaptive stability design can\ngreatly improve the navigating control and effectively overcome the ADHDP algorithm limitation. Thus, this adaptive control can\nbe one of the intelligent ADHDP control methods. Furthermore, this method will be a foundation for the development of an\nintelligent USV controller.
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