Ship Heading Model Parameter Identification Based On EKF And L₂ Closed-Loop Forming Filter Control

The main purpose of this paper is to solve the problem of obtaining the ship model parameters.Furthermore,the L2 closed-loop shaping filter heading controller is designed based on parameter identification."BAICHUAN" unmanned ship of Dalian Maritime University was used as the experimental object of this subject.The system parameters of the ship model and design of the ship motion cdntroller were successful on the basis of the structure of the ship's nonlinear motion model.This paper includes the following aspects:Firstly,the mathematical model of ship motion was derived,and the parameters to be identified were analyzed.Recursive least squares(RLS)method and extended Kalman filter(EKF)were also introduced,and these two algorithms were used to identify the parameters of ship motion model.Secondly,sailing at sea,the ship's yaw is always subject to external interference.These issues make the design of robust controllers for autonomous driving systems difficult.Using the closed-loop shaping filters to improve the robust control of L2 gain,L2 closed-loop shaping filter control was proposed.This paper described the design of L2 gain robust controller for ship steering autopilot,and the stability and robustness of the proposed controller based on Lyapunov method was proved.A large number of experimental data were obtained in the test by the"BAICHUAN" unmanned ship,and the data was de-singularized.The validity of the identified parameter model is verified to ensure that reliable data is obtained for identification experiments.The Z-type and cycle comparison tests were performed to prove that the results of EKF identification parameters were more accurate.Two sets of comparative experiments were designed.The results of the two identification algorithms were used in the design of the L2 gain robust controller and compared with the PID controller to verify the effectiveness of the controller proposed.And the controller designed by the EKF identification result has more superior performance in course keeping.