Research on unmanned aerial vehicle modeling and control based on intelligent algorithms

Abstract

This article designs an automatic flight control system for an unmanned aerial vehicle helicopter. The differential evolution intelligent algorithm is used for a state-space model identification; the differential evolution method has an advantage of choosing initial point randomly. The accuracy of the identified model is verified by comparing the model-predicted responses with the responses collected during flight experiments. The reliability and efficiency of the differential evolution algorithm are demonstrated by the experimental results. A robust controller is designed based on the identified model for the unmanned aerial vehicle helicopter with two-loop control frame: the outer-loop is used to obtain the expected attitude angles through reference path and speed with guidance-based path-following control, and the innerloop is used to control the attitude angles of helicopter tracking the expected ones with H ' loop-shaping method. The greatest common right divisor method is used to choose the weighting matrix in loop shaping, in which the stability margin is larger and has a greater bandwidth of the unmanned aerial vehicle system. Finally, a space spiral curve trajectory tracking simulation is conducted to illustrate the efficiency of the proposed control systems, and the simulation results prove that the unmanned helicopter system achieves a top-level control performance.