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 inner-loop is used to control the attitude angles of helicopter tracking the expected ones with [Formula: see text] 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.
Highlights
In recent years, unmanned vehicles have attracted a great deal of interest from the university, the industry, and the military world
The past decade has seen a golden age in the development of unmanned aerial vehicle (UAV), but there are only a few documented examples of small-scale unmanned helicopter applications in real-world scenarios; this is mainly due to the poor flight performance that can be achieved and guaranteed under automatic control
The UAV helicopter model is a complicated system, and the system identification method is well suited to the rotorcraft problem
Summary
In recent years, unmanned vehicles have attracted a great deal of interest from the university, the industry, and the military world. The UAV helicopter model is a complicated system, and the system identification method is well suited to the rotorcraft problem. A nonlinear search based on differential evolution (DE) intelligent algorithm is conducted for a 6-degree-offreedom (DoF) linear state-space model that matches the frequency-response dataset; the DE method has an advantage of choosing initial point randomly, and the reliability and efficiency of the DE algorithm are demonstrated by the experimental results. The state-space model of UAV helicopter is established, and the stability and control derivatives are identified by matching the conditioned frequency responses x_ = Ax + Bu ð3Þ y = Cx + Du where x = 1⁄2 Du Dv Dw Dp Dq Dr Df Du Dc T u = 1⁄2 Ddcol Ddped Ddlat Ddlon T. A and B are stability derivatives and control derivatives we can see from Table 1 that which are shown as follows
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