输入饱和与姿态受限的四旋翼无人机反步姿态控制

本文针对四旋翼无人机研究了鲁棒反步姿态控制策略.由于四旋翼无人机结构复杂,其非线性数学模型难以精确建立,因此在控制器设计过程中需要综合考虑模型不确定性、未知外部干扰、输入饱和以及姿态受限等因素.针对模型中的不确定项,使用神经网络进行逼近;对于外部未知干扰,使用非线性干扰观测器进行补偿;使用双曲正切函数逼近饱和函数,解决输入饱和问题;同时使用界限Lyapunov函数设计控制器,确保姿态满足限制条件.最后,设计四旋翼无人机反步姿态控制器,并根据Lyapunov稳定性定理证明了闭环控制系统的有界稳定.仿真结果表明了所研究控制方法的有效性.     

基于多传感器的四旋翼飞行器硬件系统设计

四旋翼飞行器由于其简单的机体结构与较为复杂的姿态控制,近年来在军用和民用领域广泛应用。旨在通过四旋翼飞行器飞控平台的搭建,实现对飞行器姿态的稳定控制。首先论述了四旋翼飞行器的飞行原理与机械结构,给出了硬件系统总体结构。在对各功能模块整合的基础上,实现基于多传感器的控制系统硬件电路设计。仿真与实验证明：多传感器使用过程中,通过卡尔曼滤波进行姿态数据的融合,有效地解决了加速度计、陀螺仪易受外界干扰问题,所设计硬件系统在飞行实验中性能稳定,为四旋翼的稳定控制提供了参考。     

针对飞行器的Muti-Agent仿真测试床的设计与实现

飞行器的Muti-Agent测试床是研究多无人机协同算法的重要平台。首先基于HLA标准完成了测试床构架的总体设计,然后通过测试床软件体系结构设计、运行流程设计、接口设计3个方面完成了整个系统的详细设计,最后通过使用VC++作为开发工具,采用地形LOD、MAPX控件、飞行器系统建模等方法实现了针对飞行器的Muti-A-gent分布式三维仿真测试床。仿真实例表明,该系统具有较强的通用性和逼真性。     

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects. Recommended by Editorial Board member Hyo-Choong Bang under the direction of Editor Hyun Seok Yang. This work was supported by the Korea Research Foundation Grant (MOEHRD) KRF-2005-204-D00002, the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) R0A-2007-000-10017-0 and Engineering Research Institute at Seoul National University. Daewon Lee received the B.S. degree in Mechanical and Aerospace Engineering from Seoul National University (SNU), Seoul, Korea, in 2005, where he is currently working toward a Ph.D. degree in Mechanical and Aerospace Engineering. He has been a member of the UAV research team at SNU since 2005. His research interests include applications of nonlinear control and vision-based control of UAV. H. Jin Kim received the B.S. degree from Korea Advanced Institute of Technology (KAIST) in 1995, and the M.S. and Ph.D. degrees in Mechanical Engineering from University of California, Berkeley in 1999 and 2001, respectively. From 2002–2004, she was a Postdoctoral Researcher and Lecturer in Electrical Engineering and Computer Science (EECS), University of California, Berkeley (UC Berkeley). From 2004–2009, she was an Assistant Professor in the School of in Mechanical and Aerospace Engineering at Seoul National University (SNU), Seoul, Korea, where she is currently an Associate Professor. Her research interests include applications of nonlinear control theory and artificial intelligence for robotics, motion planning algorithms. Shankar Sastry received the B.Tech. degree from the Indian Institute of Technology, Bombay, in 1977, and the M.S. degree in EECS, the M.A. degree in mathematics, and the Ph.D. degree in EECS from UC Berkeley, in 1979, 1980, and 1981, respectively. He is currently Dean of the College of Engineering at UC Berkeley. He was formerly the Director of the Center for Information Technology Research in the Interest of Society (CITRIS). He served as Chair of the EECS Department from January, 2001 through June 2004. In 2000, he served as Director of the Information Technology Office at DARPA. From 1996 to 1999, he was the Director of the Electronics Research Laboratory at Berkeley (an organized research unit on the Berkeley campus conducting research in computer sciences and all aspects of electrical engineering). He is the NEC Distinguished Professor of Electrical Engineering and Computer Sciences and holds faculty appointments in the Departments of Bioengineering, EECS and Mechanical Engineering. Prior to joining the EECS faculty in 1983 he was a Professor with the Massachusetts Institute of Technology (MIT), Cambridge. He is a member of the National Academy of Engineering and Fellow of the IEEE.     

四旋翼无人机姿态系统复合连续快速非奇异终端滑模控制EI北大核心CSCD

本文针对受多源干扰影响的四旋翼无人机姿态系统,基于复合连续快速非奇异终端滑模算法,研究了姿态指令变化率未知情况下的连续有限时间姿态跟踪控制问题.首先,基于四旋翼无人机姿态回路动力学模型,通过引入虚拟控制量实现姿态跟踪误差动态的三通道解耦;其次,分别针对各通道跟踪误差动态设计高阶滑模观测器,实现跟踪误差变化率和集总干扰的有限时间估计;最后,结合姿态跟踪误差变化率的估计信息,构建动态快速非奇异终端滑模面,并在控制设计中用指数幂函数代替符号函数以保证控制量连续.并且基于Lyapunov分析方法给出了姿态跟踪误差有限时间收敛的严格证明,仿真结果验证了所提方法的有效性.     

Adaptive prescribed time control for quadrotor formation with stochastic links failure

The formation control for multiple quadrotors subject to maintaining the formation configuration and collision avoidance in the situation of stochastic links failure is investigated in this paper. First, the distributed formation controller is designed, the position controller is developed to manage the desired formation of position, and the attitude controller is developed to control the translation and rotation movements of the quadrotor. Then, in order to avoid the collisions between multiple quadrotors and the obstacles, a potential energy function method is introduced into the quadrotor formation control combined with the nest adaptive control. Inspired by the design of event trigger controller, a communication compensation controller is designed to ensure the stability of quadrotor formation under the condition of random communication interruption and recovery. Moreover, a prescribed time function is designed, which means the convergence time of the formation system can be set in advance. The prescribed time stability of the formation control system is proved by Lyapunov theory. Finally, the simulation results verify the effectiveness and superiority of this method.     

State transformation-based dynamic visual servoing for an unmanned aerial vehicle

In this paper, we propose a visual servoing control for a quadrotor unmanned aerial vehicle (UAV) which is based on a state transformation technique. The UAV is equipped with a single downwards facing camera, and the motion control objective is the regulation of relative displacement and yaw to a stationary visual target located on the ground. The state transformation is defined by a system of partial differential equations (PDEs) which eliminate roll and pitch rate dependence in the transformed image feature kinematics. A method for computing the general solutions of these PDEs is given, and we show a particular solution reduces to an established virtual camera approach. We treat point and line cases and introduce image moment features defined in the virtual camera image plane. Robustness of the control design is improved by accounting for attitude measurement bias, and uncertainty in thrust gain, mass, and image feature depth. The asymptotic stability of the closed-loop is proven. The method is based on a simple proportional-integral-derivative (PID) structure which can be readily implemented on-board. Experimental results show improved performance relative to previous work.     

无人机三维编队飞行的鲁棒H∞控制器设计

针对无人机编队控制中模型不确定性与外干扰同时存在的情况,首先基于无人机自身的自动驾驶仪和编队运动学关系建立了无入机编队的三维数学模型,这种建模方式物理意义明晰;进而提出一种基于鲁棒H∞控制理论的编队控制器设计方法,按前向、侧向和垂直方向3个通道分别设计控制律,降低了鲁棒控制器的调参难度,简化了三维编队控制问题.仿真结果表明了所设计的控制器的有效性,可实现无碰撞、快速、稳定地保持和调整无人机编队队形,具有良好的鲁棒性能.     

一种适用于集群无人机超分辨的波束域方位估计算法

为了提升对集群无人机目标的分辨能力,提出了一种波束域方位超分辨估计算法。该方法不仅可以避免传统的稀疏表示类测角方法的计算复杂度高和需要选取超参数问题,而且在单快拍数、入射信号空间角度间隔较小等非理想情况下都具备很好的分辨能力,与相控阵雷达波束形成、距离?多普勒域处理的流程相适配。通过仿真试验,可以说明：该方法可以帮助雷达提前将密集目标分辨开,进而实现对于集群的规模（个数）、轮廓等信息进行准确估计,具有很强的应用价值。