Research On Path Following Control Of USV In Complex Marine Environment

In recent years,with the continuous development of technology,Unmanned Surface Vehicles(USVs)have gradually entered people’s view.Compared with traditional surface vessels,USVs have the advantages of high flexibility,low cost and simple operation,so they have a wide application prospect and play an important role in both military and civilian fields.When USV moves in Complex Marine Environment,it will be disturbed by environmental forces such as wind,waves and ocean currents,which will affect the accuracy of USV motion.Path following is the main function of USV motion control system and it is one of the key technologies to achieve autonomous navigation as well as various tasks.Under this background,the path following control problem of USV in Complex Marine Environment is studied in this thesis.This thesis provides an overview of the current development status of USV both domestically and internationally,as well as an analysis and summary of the path following control algorithms used.It further explores the theoretical knowledge of USV models and the assumptions of simplified models,and establishes a three-degree-of-freedom mathematical model of USV.Two coordinate systems are introduced to describe the motion state of USV,and the kinematics and dynamics models are derived and formulated.Three model-free adaptive control methods are introduced for the difficult problem of USV heading control in complex sea conditions,and finally,Compact Form Dynamic Linearization-Model Free Adaptive Control(CFDL-MFAC)is used to control the USV heading.The robustness of CFDL-MFAC method is verified by comparing with Proportional Integral Differential(PID)controller in simulation experiments.The Line-Of-Sight(LOS)guidance algorithm is used to calculate the desired heading,an improved Adaptive Line Of Sight(ALOS)method was proposed to analyze the factors affecting the value of forward-looking distance,including lateral error and USV’s own speed,and set the value range of forward-looking distance.The algorithm calculates the appropriate forward-looking distance in real time by considering these factors during the path following process,so that the USV can correct quickly and smoothly when it deviates from the path,while improving the following performance for curved paths and increasing the versatility and flexibility of the algorithm.In addition,a reduced-order state observer is used to estimate the sideslip angle online and to compensate for it.Comparative experiments before and after the algorithm improvement are conducted on the MATLAB platform to verify the feasibility and effectiveness of ALOS.