Unmanned aerial vehicle humanoid intelligent control method

Abstract

The invention provides a humanoid intelligent control method for an unmanned aerial vehicle. The method comprises steps of determining an unmanned aerial vehicle flight mathematical model; designing ahumanoid intelligent controller; the humanoid intelligent control comprising a three-layer structure of an operation control layer, a parameter correction layer and a task adaptation layer; and finally, optimizing a proportional differential coefficient by adopting particle swarm optimization. The humanoid intelligent controller can determine a change strategy according to the deviation of the adjusted quantity and a change trend of the deviation, and a PID control parameter self-adaption problem of the four-rotor aircraft can be effectively solved, however, the proportion, the differential coefficient and the attenuation coefficient in humanoid intelligent control can influence the final control effect, so the particle swarm optimization algorithm is adopted to perform parameter optimization. Therefore, the humanoid intelligent controller based on the particle swarm algorithm can realize quick response of the angle of the unmanned aerial vehicle, and stability and anti-interference performance are improved.

Description

technical field [0001] The invention belongs to the field of unmanned aerial vehicle flight control, especially for the flight control of a single unmanned aerial vehicle, and specifically designs a humanoid intelligent control system for the unmanned aerial vehicle. Background technique [0002] Nowadays, with the development of various technologies such as flight control and micro-electromechanical systems, quadrotor aircraft are more and more applied to people's daily life. The traditional quadrotor aircraft controls the pitch angle (θ), yaw angle (ψ) and roll angle (φ) of the quadrotor aircraft by adjusting the error between the input quantity and the feedback quantity through PID, so as to complete the body in the horizontal plane. and vertical movement. However, the adjustment process of the traditional cascade PID is too cumbersome and there is no adaptability of self-tuning parameters, and there are often problems such as large errors in the quadrotor control. Comb...

AI Technical Summary

Problems solved by technology

However, the adjustment process of the traditional cascade PID is too cumbersome and there is no adaptability of self-tuning parameters, and there are often problems such as large errors in the quadrotor control. Combined new quadrotor controller—a quadrotor humanoid intelligent controller based on particle swarm algorithm

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