Adaptive navigating control based on the parallel action-Network ADHDP method for unmanned surface vessel

Abstract

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