| Since the Civil Aviation Administration of China issued the Regulations on General Aviation Flight Mission Approval and Management in 2013,the market scale of civil UAVs in China has been expanding year by year.Multi-rotor UAV can bring convenience to many civil fields.Flight control is the basic work for UAV to realize flight tasks,and flight path planning can guide the flight.This paper takes the quadrotor UAV(Unmanned Aerial Vehicle)as the platform,and studies the path planning algorithm and flight control method,which are the core contents of the autonomous flight process of the quadrotor UAV.Aiming at the global static flight path planning problem,this paper chooses to improve the elite ant colony algorithm;In view of the flight control problem of quadrotor UAV,considering the characteristics of quadrotor itself and the advantages and disadvantages of common flight control algorithms,the conventional PID(Proportion,Integral,Differential)control algorithm is integrated with the human-simulated intelligent control theory after parameter tuning.A humanoid intelligent PID controller based on the quadrotor UAV model is designed for attitude control.The function of flight path planning is very important for the realization of autonomous flight,intelligent development and flight mission of the quadrotor UAV.Different models,flight environments,mission conditions,evaluation indexes and external constraints all need to adopt corresponding path planning algorithms.In this paper,when studying the path planning algorithm of UAV in two-dimensional space,after consulting a large number of relevant literatures in recent years,the mechanism,characteristics,applicable scenarios and limitations of a variety of widely used track search algorithms are analyzed and compared.Since this paper mainly studies the global flight path planning,according to its requirements,the elite ant colony algorithm is used as the algorithm to study the flight path planning in this paper.After that,an improved guiding factor is added to the elite ant colony algorithm to improve the search efficiency of the algorithm.Finally,the original state transfer strategy and pheromone update formula of the ant colony algorithm are integrated to complete the flight path search based on the elite ant colony algorithm.It is verified that the improved route planning algorithm has higher efficiency and shorter searching track in the simulation experiment platform.To study the flight control method of the specific physical model of the quadrotor UAV,first of all,it is necessary to analyze its own characteristics and abstract its physical model into a mathematical model that can be expressed by formulas.Through the analysis of the physical model of the quadrotor UAV,it can be concluded that it has four rotors,each of which is equipped with a motor.The speed of the motor determines the force of the rotor,and the UAV can achieve the corresponding flight attitude by adjusting the different speed of the motor.Two coordinate systems were established to describe the position and attitude information of UAV,and all parameters needed for modeling were defined in two coordinate systems of the world and the airframe.The position and attitude information could be converted between the two coordinate systems by means of rotating attitude matrix.The position equation of UAV model is solved by Newton’s second law,Euler equation and model control assignment scheme.The attitude equation of the model can be obtained according to the angular momentum equation and the assumption of small Angle of attitude Angle.Finally,the mathematical model of the quadrotor UAV is established and simplified with the help of several reasonable assumptions,ignoring the individual restrictions.According to the mathematical model of UAV established above,a humansimulated intelligent PID controller is designed by analyzing the main control variables and integrating the classical PID algorithm and human-simulated intelligent control algorithm.Describes in detail the PID control and intelligent control algorithms,analyzes the advantages and disadvantages of two algorithms,and combining with the advantages of the two algorithms put forward a new control method,this method makes up for the PID control parameters as constant and the shortcoming of human-simulated intelligent control of a single ratio,good simulation results have been achieved and enhanced robustness.At the same time,aiming at the nonlinear model of UAV,the nonlinear controller is designed based on the state space equation of the reference system in the world coordinate system.The simulation experiment verifies that the nonlinear controller has achieved good results in the attitude and position control.In this study,UAV route planning algorithm in the ant colony algorithm is improved when the elite of the guiding factor and pheromone increment formulas,and by improving the human-simulated intelligent control prototype fusion algorithm and the conventional PID control algorithm is a new intelligent PID controller is designed,the route planning algorithm is verified by simulation experiment and the effect of the controller. |
