Research On Modeling And Tracking Control Method Of Unmanned Micro Helicopter

Due to the characteristics of unmanned helicopters with vertical take-off and landing and fixed-point hovering,people are paying more and more attention to it.Meanwhile,they are more and more widely used in military and civilian fields.However,unmanned helicopter is a highly nonlinear,strongly coupled,under-actuated system,which makes its control system research very challenging.In this paper,an unmanned micro-small helicopter is taken as the research object.The attitude,speed and position of the unmanned helicopter are designed as PID controller and an improved particle swarm algorithm for parameter tuning of the PID controller are proposed based on the establishment of unmanned helicopter model.A track tracking control system is designed by combining the track planning based on the improved artificial potential field method with the position control system of an unmanned helicopter.And the simulation experiments are performed on the single-machine tracking control and formation control.The main research contents of this article are as follows:1.Unmanned helicopter modeling.Firstly,a non-linear mathematical model of unmanned helicopter is established by analysis method which taking the ALIGN TREX600 unmanned helicopter as a specific object.Then,linearize the unmanned helicopter nonlinear model with a small disturbance equation and establish a linearized space state model of the unmanned helicopter.This model can fully describe the actual situation of unmanned helicopter.2.Unmanned helicopter tracking control system design.The PID controller is designed for the attitude,speed and position of unmanned helicopter based on the linearization model of unmanned helicopter.Then,an improved particle swarm optimization algorithm（IPSO）is proposed to set the controller parameters.Firstly,this paper obtains an improved particle swarm optimization algorithm by improving the inertia factor w,modifying the particle swarm velocity v_t+1 and adding the adaptive mutation factor R because of the defects of traditional particle swarm optimization.The simulation experiments verify the feasibility of the IPSO optimization algorithm.Secondly,the parameters of the unmanned helicopter control system are adjusted by combining IPSO-PID with the input control quantity.Finally,the spiral trajectory is used to verify the tracking ability of the control system to complex trajectories after tuning.3.Real-time flight path planning and tracking control for unmanned helicopters.An improved artificial potential field method is proposed as a real-time track planning algorithm and the planned track is input into the unmanned helicopter control system as expected.The single aircraft track tracking control system reserves the safe distance of the unmanned helicopter during track planning.Based on the track guidance mode of the single tracking control system,the formation track tracking control system adds the interaction between the unmanned helicopters and continuously feedbacks the position of the unmanned helicopter control system to the track planning to prepare for the next track planning point.Finally,the flight path of unmanned helicopter and the theoretical flight path of artificial potential field planning are simulated.4.Simulation experiment of unmanned helicopter tracking control system.The experimental results show that the single-aircraft control system achieves the destination by obstacle avoidance flight under the real-time guidance of the expected trajectory.In addition,the coincidence degree of actual track and theoretical track is high.Moreover,the tracking effect of irregular single track is better.The formation control system has a better tracking effect on formation tracks.The formation maintenance,separation and reconstruction effects are better under the interaction force.The above experiments verify the effectiveness and fast convergence of the control system.