Identification and control of a small-scale helicopter

Abstract：Designing reliable flight control for an autonomous helicopter requires a high performance dynamics model.In this paper,a nonlinear autoregressive with exogenous inputs(NLARX)model is selected as the mathematical structure for identifying and controlling the flight of a small-scale helicopter.A neural network learning algorithm is combined with the NLARX model to identify the dynamic component of the rotorcraft unmanned aerial vehicle(RUAV).This identification process is based on the well-known gradient descent learning algorithm.As a case study,the multiple-input multiple-output(MIMO)model predictive control(MPC)is applied to control the pitch motion of the helicopter.Results of the neural network output model are closely match with the real flight data.The MPC also shows good performance under various conditions. 作者： Author： Abdelhakim DEBOUCHA[1]  Zahari TAHA[2] 作者單位： Centre for Product Design and Manufacturing,University of Malaya,50603 Kuala Lumpur,MalaysiaFaculty of Manufacturing Engineering and Management Technology,University Malaysia Pahang,26300 Gambang,Pahang,Malaysia 期 刊： 浙江大學(xué)學(xué)報(bào)（英文版）（A輯：應(yīng)用物理和工程）   ISTICEISCI Journal： JOURNAL OF ZHEJIANG UNIVERSITY SCIENCE A 年，卷(期)： 2010, 11(12) 分類號(hào)： V249.1 TP242 Keywords： Dynamics model    System identification    Black box    Small-scale helicopter    Neural networks(NNs)    Control design    機(jī)標(biāo)分類號(hào)： TP3 TP1 機(jī)標(biāo)關(guān)鍵詞： learning algorithm    multiple-input multiple-output    neural network    model    high performance    predictive control    gradient descent    flight control    case study    autonomous    structure    dynamic    MPC    paper    based    MIMO 基金項(xiàng)目： the MOST/(Malaysia)Sciencefund:Hardware-in-the-Loop Simulation for Control System of Mini Scale Rotorcraft

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