Research On Adaptive Dynamic Surface Control For Unmanned Sailboat Course

In recent years,unmanned sailboat has shown its unique value as an intelligent vehicle on the sea,especially in marine resource exploration and environmental monitoring,which can improve the utilization rate of time and space,reduce fuel consumption and environmental pollution.The unmanned sailboats affected by the effects of wind sail and currents are prone to lateral drift during navigation,causing problems such as deviations from the route.Improving the sailing efficiency of unmanned sailboat and realizing its adaptive control is an urgent scientific problem to be solved in promoting the development of unmanned sailboat.This paper mainly focuses on the course adaptive control of unmanned sailboat.Firstly,a neural network based adaptive dynamic surface course control method is proposed for the case that the course keeping problem of 4 DOF unmanned sailboat has model uncertain part,both the control direction and the external environment disturbances are unknown.In this strategy,the neural network is used to approximate the model's uncertain.The problem of unknown control gain is properly solved by using Nussbaum gain function.Dynamic surface control technique is introduced to eliminate the "computational expansion"problem of the Backstepping method.Secondly,to solve the course control problem for unmanned sailboat with control input saturation,a neural network based adaptive recursive sliding-mode dynamic surface course control method is proposed in the presence of the model uncertainties?unknown external disturbances and unknown control direction.This method transforms the system form to deal with the problem of unknown control direction by low-pass filter.The hyperbolic tangent function and Nussbaum function are used to handle the input constraint.A recursive sliding-mode dynamic surface is adopted to avoid being fragile to the perturbation in both the filter time constant and adaptive parameters of neural network in the traditional dynamic surface control.Thirdly,to solve the course control problem with input saturation,a minimal learning parameter(MLP)based adaptive recursive sliding-mode dynamic surface course control method is proposed for the unmanned sailboat non-affine course motion model.The non-affine system is first transformed into a time-varying system with a linear structure using the Taylor expansion method.The adaptive backstepping control method and the Nussbaum function are introduced to design the controller.The MLP method is combined with the recursive sliding mode dynamic surface control technology to reduce the computational load of the proposed algorithm.Finally,the simulation results based on a 12m unmanned sailboat are carried out,and the effectiveness of the proposed control methods is verified.The results show that the unmanned sailboat course control response speed is fast.The proposed controller has strong robustness for external environmental disturbances and unknown model section,which can achieve the control odjectives.