【摘要】：四旋翼無人機(Quadrotor Unmanned Aircraft Vehicle,QUAV)具有結(jié)構(gòu)簡單、成本低廉、性能卓越以及飛行控制方式獨特等特點,成為近年來無人機領(lǐng)域的研究熱點。在執(zhí)行室內(nèi)環(huán)境下的監(jiān)視和偵察任務(wù)中,QUAV優(yōu)勢明顯,具有廣闊的應(yīng)用前景。本文圍繞室內(nèi)環(huán)境下QUAV的定位與控制問題,給出了QUAV的非線性數(shù)學模型,使用同步定位與地圖構(gòu)建(Simultaneous Localization and Mapping,SLAM)技術(shù)解決QUAV的室內(nèi)定位問題,并根據(jù)SLAM獲得的位姿信息進一步研究受到建模不確定、外部干擾、輸入飽和以及姿態(tài)受限等因素影響的QUAV控制方法。論文主要研究內(nèi)容如下:首先,根據(jù)QUAV運動特點,研究了QUAV非線性數(shù)學模型。為了后續(xù)研究方便,將QUAV數(shù)學模型劃分為由姿態(tài)環(huán)組成的快回路方程和由位置環(huán)組成的慢回路方程,并轉(zhuǎn)換得到了具有仿射非線性方程形式的QUAV快回路和慢回路系統(tǒng)方程。然后,針對室內(nèi)環(huán)境下QUAV的定位問題,綜合使用激光測距儀、姿態(tài)傳感器和高度傳感器,研究了基于迭代最近點(Iterative Closest Point,ICP)方法的SLAM算法。針對激光數(shù)據(jù)和高度數(shù)據(jù)的歪斜問題,利用姿態(tài)傳感器的姿態(tài)角數(shù)據(jù)進行數(shù)據(jù)修正;針對QUAV的室內(nèi)定位問題,應(yīng)用ICP匹配算法予以處理;針對地圖構(gòu)建問題,分別給出了柵格地圖和幾何地圖的創(chuàng)建與更新步驟。SLAM實驗結(jié)果表明了ICP定位算法穩(wěn)定性好、精度高,并根據(jù)SLAM地圖創(chuàng)建結(jié)果對比了兩種地圖表示方式。其次,針對QUAV的快回路系統(tǒng)和慢回路系統(tǒng),考慮外部未知干擾,研究了一種基于干擾觀測器的反步控制方法。設(shè)計干擾觀測器在線逼近由系統(tǒng)建模誤差和外部干擾構(gòu)成的復合干擾,并根據(jù)干擾估計值設(shè)計反步控制策略,通過Lyapunov定理嚴格證明了閉環(huán)系統(tǒng)的穩(wěn)定性。仿真結(jié)果表明,在存在建模誤差和外部干擾的情況下,QUAV快慢回路系統(tǒng)均具有良好的跟蹤性能。接著,針對QUAV的快回路系統(tǒng),給出了一種具有建模不確定、外部干擾、輸入飽和與姿態(tài)受限的反步控制方法。針對建模不確定,使用神經(jīng)網(wǎng)絡(luò)進行逼近;針對復合干擾,設(shè)計非線性干擾觀測器對干擾進行補償;針對輸入飽和,使用雙曲正切函數(shù)逼近飽和函數(shù);針對輸出受限問題,使用界限Lyapunov函數(shù)設(shè)計控制器,保證姿態(tài)滿足限制條件。通過Lyapunov方法證明了閉環(huán)系統(tǒng)的所有信號半全局一致有界。仿真結(jié)果表明在具有建模不確定、外部干擾、輸入飽和與姿態(tài)受限的情況下,所設(shè)計控制方法可得到滿意的控制效果。最后,搭建了室內(nèi)環(huán)境下QUAV的SLAM實驗平臺,分別在室內(nèi)環(huán)境下的小場景區(qū)域和大場景區(qū)域中進行實驗,驗證了室內(nèi)環(huán)境下SLAM算法的可行性和有效性。
[Abstract]:(Quadrotor Unmanned Aircraft Vehicle,QUAV) with simple structure, low cost, excellent performance and unique flight control mode has become a research hotspot in the field of UAV in recent years. QUAV has obvious advantages in carrying out surveillance and reconnaissance missions in indoor environment and has broad application prospects. In this paper, the nonlinear mathematical model of QUAV is given around the problem of positioning and control of QUAV in indoor environment, and the problem of indoor location of QUAV is solved by using synchronous location and map building (Simultaneous Localization and Mapping,SLAM) technology. According to the position and attitude information obtained by SLAM, the QUAV control method which is affected by modeling uncertainty, external interference, input saturation and attitude limitation is further studied. The main contents of this paper are as follows: firstly, according to the characteristics of QUAV motion, the nonlinear mathematical model of QUAV is studied. For the convenience of further study, the QUAV mathematical model is divided into fast loop equations composed of attitude loops and slow loop equations composed of position loops, and QUAV fast loop and slow loop system equations with affine nonlinear equations are obtained. Then, aiming at the localization problem of QUAV in indoor environment, the SLAM algorithm based on iterative nearest point (Iterative Closest Point,ICP (Iterative Closest Point,ICP) method is studied by using laser rangefinder, attitude sensor and height sensor. Aiming at the skew problem of laser data and altitude data, the attitude angle data of attitude sensor is used to correct the data; to solve the indoor positioning problem of QUAV, ICP matching algorithm is used to deal with the problem; to solve the problem of map construction, The steps of creating and updating raster map and geometric map are given respectively. The results of SLAM experiment show that the ICP localization algorithm has good stability and high precision, and the two kinds of map representation methods are compared according to the results of SLAM map creation. Secondly, for the fast loop system and slow loop system of QUAV, a backstepping control method based on disturbance observer is studied, considering the external unknown disturbance. The disturbance observer is designed to approach the complex disturbance which consists of modeling error and external disturbance. According to the disturbance estimation, the backstepping control strategy is designed. The stability of the closed-loop system is strictly proved by Lyapunov theorem. Simulation results show that QUAV fast and slow loop systems have good tracking performance in the presence of modeling errors and external disturbances. Then, for the fast loop system of QUAV, a backstepping control method with modeling uncertainty, external disturbance, input saturation and attitude limitation is proposed. For modeling uncertainty, neural network is used to approximate; for complex disturbance, nonlinear disturbance observer is designed to compensate for disturbance; for input saturation, hyperbolic tangent function is used to approximate saturation function; for the problem of limited output, The limit Lyapunov function is used to design the controller to ensure that the attitude meets the limiting conditions. It is proved that all the signals of the closed loop system are semi-globally uniformly bounded by the Lyapunov method. The simulation results show that the proposed control method can obtain satisfactory control results under the conditions of uncertain modeling, external interference, input saturation and attitude limitation. Finally, the SLAM experimental platform of QUAV in indoor environment is built, and the experiment is carried out in the small scene area and large scene area in the indoor environment, which verifies the feasibility and effectiveness of the SLAM algorithm in the indoor environment.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：V279

【參考文獻】

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本文編號：2199915