一种小型无人机地面控制站软件的开发

地面控制站是整个小型无人机系统的重要组成部分.首先介绍了一个无人直升机地面控制站的设计和实现,分析了实现该地面站的三项重要功能模块.在此基础上,讨论了实现功能模块所涉及的关键技术问题.在Linux操作系统下.以NetBeans 6.5为开发平台使用Java语言开发而成.通过定时器的引入,在实时接收飞行数据的同时,实现数据存储与显示、任务规划、数据回放的功能.经过多次实际飞行试验的使用,系统达到设计要求.     

一种小型无人直升机自主起飞控制方法

针对小型无人直升机(Small-scale unmanned helicopter, SUH) 起飞过程中过度依赖于地面飞行手的问题, 提出了一种基于经验知识与系统辨识的自主起飞控制方法. 首先, 通过研究专业飞行手手动操纵小型无人直升机起飞过程中高度与油门、总距舵量等信息的对应关系, 分析了利用学习飞行手的操纵行为实现小型无人直升机自主起飞的可行性, 并设计了小型无人直升机自主起飞控制流程. 引入了安全高度及变增益控制以提高自主起飞过程中的飞行安全性能, 利用不完全微分控制方法抑制了微分高频噪声. 其次, 为了获取自主起飞过程中控制参数, 采用自适应遗传算法对小型无人直升机动力学模型进行了辨识, 在动力学模型的基础上进一步辨识得到了飞行控制参数. 最后, 通过在小型无人直升机平台进行的实际飞行实验, 验证了本文方法的有效性.     

小型无人直升机横纵向模型参数辨识方法

针对小型无人直升机具有多变量、非线性和强耦合的特点,提出一种基于参数辨识的横纵向通道动力学建模方法.该方法根据直升机小扰动运动学方程和小型无人直升机空气动力学特点,推导了小型无人直升机横纵向通道动力学模型.在悬停条件下通过对模型进行简化,得到小型无人直升机横纵向通道待辨识线性耦合模型.根据飞行实验数据,通过采用多变量最小二乘方法估计出该耦合模型的未知参数.模型预测数据和实际飞行数据的比较结果表明,所建模型能充分反映该小型无人直升机在悬停状态下的横纵向通道动力学特性,具有较高精度且结构相对简单,可作为自主飞行控制器设计的参考模型.     

基于预测误差法小型无人直升机系统辨识

小型无人直升机是一个复杂的非线性系统.为了真正实现小型无人直升机的自主飞行,须对其进行数学建模.本文重点分析了Raptor90小型无人直升机悬停时横、纵向通道的输入输出关系,通过严格推导得到横、纵向通道通的参数化模型.通过试验采集得到输入输出数据,利用基于预测误差法的输出误差模型进行系统辨识.模型预测数据与实际飞行实验室数据的比较表明,所建模型很好的反映了小型无人直升机在悬停状态下的动力学特性,可在该状态下基于此模型进行飞行控制器的设计.     

基于最小二乘与自适应免疫遗传算法的小型无人直升机系统辨识

针对小型无人直升机不稳定、强耦合、非线性的特点,建立了小型无人直升机悬停状态下飞行动力学模型.设计了一种基于最小二乘与自适应免疫遗传算法(LS-AIGA)的辨识算法,根据辨识实验的需要研制了机载微小型导航、制导与控制系统(MGNC).利用飞行实验数据,根据本文的辨识算法,对所建立模型中未知参数进行了辨识.最后对得到的模型进行了验证与分析,结果表明模型辨识数据与真实飞行实验数据匹配较好,所建立模型能够反映小型无人直升机动力学特性.     

基于Matlab和VC++混合编程的无人机地面控制站实现

通过分析无人机(UAV)系统对地面控制站的需求,介绍了地面控制站的结构和功能.鉴于Matlab在数值分析和图形处理上的优势,提出了采用Matlab和VC++混合编程技术实现地面控制站的数据分析功能.根据UAV对不同飞行任务的需求,实现了在同一地面控制站中两种坐标系下导航电子地图的自由切换.该地面控制站具有实时性强、稳定性好、人机界面友好等优点,已在某型号UAV飞行实验中得到了充分的检验与应用.     

小型无人直升机悬停状态自适应模糊PID控制器设计

小型遥控直升机在实际应用中主要是完成航拍,为了实现小型直升机水平姿态的自动平衡,将其升级为无人直升机.针对小型直升机的降阶模型,提出了一种便于单片机实现的、针对悬停作业时小型无人直升机自适应模糊PID控制器的设计方法,能对PID参数进行动态整定,实现无人直升机悬停状态时水平姿态的平衡,仿真实验表明,自适应模糊PID控制器的动态性能好、稳态精度高、鲁棒性较强,宜适用于小型无人直升机的增稳控制.     

小型无人直升机着陆高度单目视觉测量方法

针对小型无人直升机自主降落过程中姿态发生变化和非差分GPS高程精度不高的情况,研究了一种基于单目视觉的小型无人直升机离地高度测量方法,用于提高自主降落的品质。在机体姿态角已知的情况下,识别提取降落图标中相互平行的特征直线段,并根据机载摄像头投影模型,实时求解机体降落过程中的离地高度。在测量系统上进行了实体实验,验证了所提出的方法,实验结果表明:该方法特征识别率高,在机体平面与地面存在倾角时,输出结果的实时性与准确性亦均能满足小型无人直升机自主降落要求。     

小型直升机LINUX仿真系统构建

无人直升机是一个极具挑战性的多学科前沿性研究课题.介绍了小型直升机LINUX仿真系统的构建过程及其相关软件实现.首先给出了小型直升机飞行控制系统和仿真系统的整体结构,并针对这两个系统结构做了简要说明.介绍了在LINUX平台下,采用FLTK图形界面库和Mesa/OpenGL实现小型直升机3D飞行和状态控制,可视化仿真及实时运动特性再现的过程.仿真系统的结果表明.仿真系统具有独特的优点和良好的开发潜力,可以辅助实际飞行实验进行无人直升机的研究.     

基于Dubins曲线的无人直升机轨迹规划

为提高无人直升机的控制性能,提出了一种基于Dubins曲线的轨迹规划算法,并对其各个部分的实现进行了研究和设计。该算法利用Dubins曲线原理对定点飞行任务的两点或者多点目标进行分析计算,寻找出一条最短的飞行路径,从而提高了飞行效率。根据无人直升机系统多变量、非线性和强耦合的特点,采用串级PID方法设计了飞行控制器,该控制器能够修正无人直升机的姿态和位置,从而提高了轨迹规划的稳定性和准确性。最后,以某小型无人直升机为实验平台表明了该轨迹规划算法和控制器的可行性。     

小型无人直升机控制律设计与仿真

依据摄动线化原理,本文对模型直升机非线性模型在平衡点进行了小摄动线性化处理,得出了线性化动力学模型,并对它进行了PID控制律设计,应用MATLAB/Simulink对控制律作用于线性化模型和非线性模型分别进行了仿真验证,结果表明作用于线性化模型具有良好控制效果的控制律尚不能有效控制非线性模型.使用backstepping方法对非线性模型子系统选择相应的李雅普诺夫候选函数,应用递归方法设计了使型直升机动力学非线性模型镇定的控制律,确保李雅普诺夫候选函数的导数为负定.经仿真验证表明利用该设计方法得到的控制律能对非线性模型进行有效控制.     

Self-Healing Control Design under Actuator Fault Occurrence on Single-rotor Unmanned Helicopters

Actuator faults are inevitable but affect reliability and safety of unmanned helicopters (UHs), especially when there are actuator constraints. In this paper, self-healing control, which is an extended active fault-tolerant control (FTC) method with reference redesign on-line, is proposed to analyze and to guarantee the safety of single-rotor UHs (SUHs) under both actuator faults and constraints. The safety includes body safety and mission safety. More specifically, body safety represents the stability of SUH itself and mission safety represents mission accomplishment with acceptable performance, furthermore, set-point tracking mission is considered. The main contribution of this paper is to analyze and to guarantee the safety of SUHs by solving a set of Linear Matrix Inequalities (LMIs) at one time. The set of LMIs includes saturation compensator design and stability guaranty with a given controller in the absence of actuator constraints, actuator fault compensation analysis, reference reachability analysis and reference redesign. On the other hand, by adding swashplate configuration, SUH model with real actuator outputs as control inputs is constructed which can describe actuator faults more clearly compared to SUH models with nominal control inputs. Finally, the proposed self-healing control method is illustrated by simulation with a nonlinear SUH model.     

Hierarchical growing cell structures: TreeGCS

We propose a hierarchical clustering algorithm (TreeGCS) based upon the Growing Cell Structure (GCS) neural network of B. Fritzke (1993). Our algorithm refines and builds upon the GCS base, overcoming an inconsistency in the original GCS algorithm, where the network topology is susceptible to the ordering of the input vectors. Our algorithm is unsupervised, flexible, and dynamic and we have imposed no additional parameters on the underlying GCS algorithm. Our ultimate aim is a hierarchical clustering neural network that is both consistent and stable and identifies the innate hierarchical structure present in vector-based data. We demonstrate improved stability of the GCS foundation and evaluate our algorithm against the hierarchy generated by an ascendant hierarchical clustering dendogram. Our approach emulates the hierarchical clustering of the dendogram. It demonstrates the importance of the parameter settings for GCS and how they affect the stability of the clustering     

Toward optimizing a self-creating neural network

This paper optimizes the performance of the growing cell structures (GCS) model in learning topology and vector quantization. Each node in GCS is attached with a resource counter. During the competitive learning process, the counter of the best-matching node is increased by a defined resource measure after each input presentation, and then all resource counters are decayed by a factor alpha. We show that the summation of all resource counters conserves. This conservation principle provides useful clues for exploring important characteristics of GCS, which in turn provide an insight into how the GCS can be optimized. In the context of information entropy, we show that performance of GCS in learning topology and vector quantization can be optimized by using alpha=0 incorporated with a threshold-free node-removal scheme, regardless of input data being stationary or nonstationary. The meaning of optimization is twofold: (1) for learning topology, the information entropy is maximized in terms of equiprobable criterion and (2) for leaning vector quantization, the use is minimized in terms of equi-error criterion.     

一个基于广义混沌同步系统的数字水印算法

混沌同步的发现为有关混沌的保密通讯研究提供了新的拓广空间。根据广义混沌同步的基本理论构建了一个新的广义混沌同步系统(GCS);利用GCS,提出了一个基于小波变换的鲁棒性数字水印算法。数值模拟实例对该文的理论结果进行了验证,理论分析及实验结果表明,给出的数字水印算法对常用的图像处理方法有较好的鲁棒性。     

Discovering genres of online discussion threads via text mining

As course management systems (CMS) gain popularity in facilitating teaching. A forum is a key component to facilitate the interactions among students and teachers. Content analysis is the most popular way to study a discussion forum. But content analysis is a human labor intensity process; for example, the coding process relies heavily on manual interpretation; and it is time and energy consuming. In an asynchronous virtual learning environment, an instructor needs to keep monitoring the discussion forum from time to time in order to maintain the quality of a discussion forum. However, it is time consuming and difficult for instructors to fulfill this need especially for K12 teachers. This research proposes a genre classification system, called GCS, to facilitate the automatic coding process. We treat the coding process as a document classification task via modern data mining techniques. The genre of a posting can be perceived as an announcement, a question, clarification, interpretation, conflict, assertion, etc. This research examines the coding coherence between GCS and experts’ judgment in terms of recall and precision, and discusses how we adjust the parameters of the GCS to improve the coherence. Based on the empirical results, GCS adopts the cascade classification model to achieve the automatic coding process. The empirical evaluation of the classified genres from a repository of postings in an online course on earth science in a senior high school shows that GCS can effectively facilitate the coding process, and the proposed cascade model can deal with the imbalanced distribution nature of discussion postings. These results imply that GCS based on the cascade model can perform as an automatic posting coding system.     

Externally Growing Cell Structures for Data Evaluation of Chemical Gas Sensors

Based on Fritzke’s GCS (Growing Cell Structures), we present here a new incremental self-organising neural network, the Externally Growing Cell Structures (EGCS). Our goals are to speed up the convergence and to improve the generalisation performance. The mechanism of internally growing cells in EGCS is the same as in GCS. However, when the Maximum Resource Vertex (MRV) or the Maximum Error Vertex (MEV) is a boundary node, the new cell is grown externally. Simulation results on neural network benchmarks, two-spiral problem and sonar mine/rock separation, indicate that EGCS performs better than the original GCS, measured by classification rate and the required number of epochs. As a new classification and regression method, the EGCS for Data Evaluation of Chemical Gas Sensors is introduced.