Research On Adaptive Internal Model Control Method Of Ship Heading

Unmanned Surface Vessel(USV),referred to as unmanned vessel,has the characteristics of fast speed,strong maneuverability,and strong autonomy.The trend of unmanned boat technology was first used in the military field,and in recent years it has been more widely used in the civilian field.Since the unmanned boat carries different task loads when performing different tasks,the control parameters of the unmanned boat will change,and the controller parameters need to be readjusted.Aiming at the problems of cumbersome and complex parameter adjustment and weak adaptive ability of the traditional control method,this paper aims at unmanned The self-adaptive control method of the course of the manned boat has been studied,and the research is mainly carried out in the following aspects:First of all,according to the motion characteristics of the unmanned boat,the motion coordinate system of the unmanned boat is established,and the ship,paddle,and rudder are regarded as a whole,and the overall mathematical model,namely the Abkowitz model,is derived.Considering that the research focus of this paper is heading control,the linear heading Nomoto response model is deduced through the linear Abkowitz model,and only the relationship between the current steering angle and the rudder angle is considered.In addition,the steering gear model of the unmanned boat is established to provide a mathematical model basis for the design of the heading adaptive controller in this paper.And by introducing the method of forgetting factor,the recursive least squares method with forgetting factor is used to improve the problem that the traditional online identification method of USV control parameters is not sensitive to new data,and improve the followability of the identification algorithm to the parameters.And based on the Matlab platform,the above identification algorithm is simulated and compared to verify the superiority of the method in this paper,and provide accurate model parameters for the design of the adaptive controller.Secondly,in order to realize heading adaptive control,a combination of recursive least squares identification algorithm with forgetting factor and internal model control is proposed.The controller designed in this paper has only one adjustable parameter,and the other parameters are the same as those of the USV.The control parameters are related,and the parameters of the USV controller are also determined after the identification is completed.Moreover,for the traditional one-degree-of-freedom internal model control and two-degree-of-freedom internal model control methods,when the system has rudder load interference,there is a static error problem after stabilization,and an improved two-degree-of-freedom internal model control method is proposed to effectively solve the problem The static error caused by rudder deviation is eliminated.In addition,for the problem that internal model control is not conducive to practical engineering applications,an internal model PID adaptive control method is proposed,which inherits the advantages of internal model control and solves the problem of practical application.And use the Matlab/Simulink platform to carry on the simulation contrast experiment to the above-mentioned designed control scheme,verify the feasibility of the scheme.Finally,on the basis of theoretical research,the software and hardware system of the unmanned boat was designed and realized in combination with the actual project,and the experimental platform of the unmanned boat was built.Based on the platform,the real ship verification of the method in this paper was completed in the waters of Songshan Lake,Dongguan City,Guangdong Province Experiment,and through the analysis of the data in the experiment,it is verified that the heading adaptive control method designed in this paper has strong followability and robustness.It can effectively reduce the cost of parameter adjustment,improve the self-adaptive ability of the unmanned vehicle to the complex environment,and realize the self-adaptive control of the course.