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Application of RBF-ARX Model-based LQR Control on Quad-Rotor Helicopter Simulator

Author: ChenQing
Tutor: PengHui
School: Central South University
Course: Control Science and Engineering
Keywords: Quadrotor ARX RBF-ARX LQR
CLC: TP183
Type: Master's thesis
Year: 2011
Downloads: 105
Quote: 0
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Abstract


The four - rotor aircraft is today a hot research direction . As a multi-input multi- output nonlinear with coupling object , its analysis and research is of great significance . Quadrotor traditional research methods are based on the physical model , through the mechanism of analysis to get the differential equation , thereby establishing a mathematical model . Has inevitable drawbacks of this method , the physical parameters is difficult to accurately measure , in the modeling process , there are some simplified linear processing . These also directly affect the accuracy of the model . In order to overcome the shortcomings of the traditional mechanistic model , the identification model has been used on a four-rotor . ARX ??model is a common linear model , in the range of local linear approximation effect is very superior . In order to describe the quadrotor this typical nonlinear object using the the ARX model portfolio . Divide the working range of the four-rotor , an ARX model to characterize the properties of each individual sub- interval , reasonable switching mechanism to achieve global approximation purpose . RBF-ARX model is a nonlinear ARX model structure variable model . The argument is a set of characterization of nonlinear state semaphore , RBF neural network architecture real-time adjustment of the model parameters . ARX ??model , RBF-ARX model has an excellent approximation effect within the linear range of the local , the other parameters can update the self- adjustment , therefore, it also has a global adaptation characteristics . LQR typical optimal control of a control method , relies on state-space equations , optimal state feedback control to achieve the desired control effect . Based on the above three models , respectively, of its LQR controller design , simulation and real-time control . The experimental results show that , in each model , the system can quickly follow a given change varies . Through comparative analysis , RBF-ARX model has a fast response , accurate and stable control effect .

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