Nonlinear Feedback Control Of Ship Course Keeping Based On Error Driven

In order to solve the key problems of the control of the course keeping under the dynamic change of the model parameters and the uncertainty of the external disturbance due to the insufficient accuracy of the model parameters in the course of the ship’s course keeping,this paper takes "Nonlinear feedback control of ship course keeping" as the topic,fully considering the actual requirements of ship navigation and heading maintenance,conducts scientific and systematic exploration from the theoretical level,and expects to provide complete technical support and scientific theoretical support for the course keeping control problem of nonlinear ship systems,and further improve the energy saving and safety of the autopilot.In this paper,two key problems of solving the ship response mathematical model and control theory algorithm in the course keeping control system are explored.Both the mathematical model and the controller research in this paper incorporate the idea of nonlinear feedback,and radial basis neural network and adaptive technology are used to make the controller more robust.First of all,considering the navigation practice,the ship response mathematical model is selected,and the validity of the mathematical model is verified by the comparison between the simulated gyration test and the real ship gyration test.In order to solve the problem that the original responsive mathematical model identification algorithm is difficult to identify parameters quickly and accurately when there are few data samples,nonlinear feedback and multi-innovation system identification technologies are used,a nonlinear innovation improved maximum likelihood estimation parameter identification algorithm is proposed.Secondly,through three simulation cycle tests,self-propelled ship model cycle tests and real ship simulator cycle tests,it is verified that the proposed improved identification algorithm has the advantage of quickly and accurately identifying model parameters in the case of fewer data samples.Finally,in order to improve the robustness of the control system,a simple nonlinear feedback technology is used,and a simple nonlinear feedback controller is designed.After simulation verification,the controller has the advantages of energy saving and strong robustness.On this basis,an event-triggered mechanism is introduced and an event-triggered nonlinear feedback controller is designed,which can reduce the wear of the actuator and save the network communication resources.Then,considering the problem of time-varying disturbance,this paper uses adaptive technology to estimate the time-varying disturbance,and combines with the minimum learning parameter method to propose a robust adaptive nonlinear feedback control algorithm.The algorithm is verified by simulation to have strong robustness.Finally,in order to further solve the problem of uncertainty of model parameters and time-varying disturbance uncertainty,nonlinear feedback technology is used and substituted into the construction process of Lypunov function,adaptive radial basis neural network is used to reconstruct model dynamic uncertainties and unknown time-varying terms,and the minimum learning parameter technique is exploited to reduce the computational load.The control rate designed in this paper is verified by numerical simulation programming method,and the effectiveness of all designed control strategies has been verified.This research provides support for the theoretical research of ship course keeping,and this paper is also of great significance to the engineering research of autopilot design.