Control of 3-DOF Helicopter Based on PID Neural Network
ZHOU Wei
WANG Xiaodong
Abstract：Using a PID neural-network multivariable controller to simulate and real-time control the affine nonlinear and strongly coupled 3-DOF Helicopter system,achieve expected effect. PID neural-network multivariable controller has strong adapt?ability. According to BP neural-network learning algorithm,initial network weights can be determined by offline simulation based on mathematical model of 3-DOF Helicopter. And through online learning neural-network weights can be updated in real-time adaptive control of nonlinear systems. Since the PID neural-network output layers cross each other,the pitch axis and horizontal roll?er of the 3-DOF Helicopter have been decoupled. The torque generated by the horizontal axis in the horizontal direction drives the rotation of the rotary shaft in the horizontal plane. Using the horizontal axis as the input,using the traditional PID controller makes the rotation axis to achieve a satisfactory control effect.
Keywords：PID controlneural networksmulti-variable control systemnonlinearself-adaptation control
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 83-88 )