Research On Path Tracking Method Of Unmanned Helicopter Based On Linear Active Disturbance Rejection Control

With the improvement of the economic level and the development of science and technology,the weapons is used by various countries on the battlefield are becoming more and more intelligent.Unmanned helicopters are used as weapons of military importance.They have been widely used on the battlefield,but unmanned helicopters.Due to the complex structure,instability,strong coupling and high nonlinearity,the design of its flight controller is very challenging.In this paper,based on the establishment of an unmanned helicopter mathematical model using the mechanism modeling method,A linear auto disturbance rejection controller(LADRC)is applied to the flight control of unmanned helicopters,and an improved particle swarm optimization algorithm(IPSO)is proposed to optimize the parameters in the cascade LADRC controller.The main research content is as follows:1.An unmanned helicopter mathematical model is established.According to the actual physical characteristics of an unmanned helicopter,the nonlinear model of the unmanned helicopter is established by analyzing the conversion rules between the machine coordinate system and the ground coordinate system.The linearization process is performed using the small perturbation method.The helicopter linearization model is obtained and simplified.The linearized model obtains a horizontally longitudinally non-coupled helicopter model,which lays a solid foundation for the design of a linear auto-disturbance rejection controller and the establishment of a flight control system.2.An improved linear auto-disturbance rejection controller is proposed and applied to the unmanned helicopter path tracking control.Through analyzing the model and structure principle of the ADRC,it is linearized to obtain a linear auto-disturbance rejection controller(LADRC),and the tracking and differentiator in the ADRC is applied to the LADRC controller.Then,we further improved the LADRC controller and designed a cascaded LADRC controller for attitude,speed,and position control of unmanned helicopters.Through helicopter flight simulation experiments,the Cascade LADRC controller enabled the unmanned helicopter to achieve fixed point in a short time.Hovering and trajectory tracking of the spiral curve,but because there are still some errors in the tracking process,and can not meet the requirements of high accuracy tracking,it will propose an optimization algorithm to optimize the controller parameters.3.An improved particle swarm optimization is proposed and applied to cascaded LADRC controller parameter optimization.Aiming at the problem that it is difficult to set high-precision parameters due to the large number of parameters in the cascaded LADRC,this paper improves the inertia weight and learning factor,and obtains an improved particle swarm intelligence algorithm(IPSO),and selects four benchmark functions for example verification.The results show that this improvement improves the algorithm's convergence time and optimization accuracy.The IPSO is applied to the parameter adjustment of the cascaded LADRC controller,and then the IPSO-LADRC controller is used in the control of the second-order transfer function system to obtain a good control effect.4.The IPSO-LADRC controller is used for the tracking control of unmanned helicopters.The simulation results are compared with the control effects before the IPSO optimization.The experimental results show that the IPSO-LADRC controller enhances the control effect of the system.It also proves that IPSO has a good parameter optimization effect for cascaded LADRC controllers.