Design Of Controller For Quadrotor UAV And Research On Its Path Planning

In recent years,the four-rotor aircraft has gradually entered the public life.Due to its increasingly diversified use,it has gradually become the focus of attention in the field of drones.Because of its clever design and strong maneuverability,it can be used indoors and outdoors,and can adapt to most weather.Four-rotor aircraft has enormous practical value,and research institutions all over the world have listed it as a key research object in drones.This paper mainly studies the flight control algorithm and path planning of the four-rotor aircraft.Firstly,through the mechanical analysis of the physical structure of the quadrotor,a mathematical model with four inputs and six outputs and six degrees of freedom is constructed,and it is written as a state equation based on matrix transformation.Second,the controller is designed to achieve aircraft stability control.The whole flight control system is divided into outer ring position control subsystem and inner ring attitude control subsystem.Considering that the quadrotor is a nonlinear,underactuated,coupled complex system,the paper adopts IBS(integral backstepping method)and LADRC(linear Self-disturbance control)combined method,because the outer ring position control has under-actuation characteristics,IBS is applied to the outer ring position subsystem,and LADRC is applied to the inner ring attitude subsystem in consideration of the attitude coupling of the inner ring attitude subsystem..In addition,the paper also compares the algorithm with the traditional classic PID with Matlab simulation.The simulation shows that the flight control algorithm designed by the paper is obviously superior to the traditional PID control,which verifies the feasibility of the algorithm designed by the paper.Thirdly,based on the APF-Q method,the path planning of the quadrotor is studied.By using the grid method to process the environment,the environment is divided into units,each unit represents a state,and eight directions of motion are set.For the action set of the quadrotor,the state-action value function is used as the evaluation function,and the path optimization is achieved by maximizing the evaluation function value.Compared with the traditional Q-learning method,the APF-Q method can improve the efficiency and the convergence speed is better than Q-learning while obtaining the same shortest path.Finally,the superiority of the APF-Q method is verified by computer simulation.