On Trajectory Tracking Model Predictive Control of an Unmanned Quadrotor Helicopter Subject to Aerodynamic Disturbances

In this article a model predictive control (MPC) strategy for the trajectory tracking of an unmanned quadrotor is presented. The quadrotor's dynamics are modeled using a hybrid systems approach and, specifically, a set of piecewise affine (PWA) systems around different operating points of the translational and rotational motions. The proposed control scheme is dual and consists of an integral MPC for the translational motions, followed by an MPC scheme for the tracking of the quadrotor's attitude motions. By the utilization of PWA representations, the controller is computed for a larger part of the quadrotor's flight envelope, which provides more control authority for aggressive maneuvering. The proposed dual control scheme is able to calculate optimal control actions with robustness against atmospheric disturbances (e.g. wind gusts) and with respect to the physical constraints of the quadrotor (e.g. maximum lifting forces or fixed thrust limitations in order to extend flight endurance). Extended simulation studies indicate the efficiency of the MPC scheme, both in trajectory tracking and aerodynamic disturbance attenuation.     

基于模型的四旋翼无人机半实物仿真平台研究

为了降低在真实飞行器上测试新的控制策略时所存在的设备损坏风险,对四旋翼无人机的控制器和半实物仿真实验平台进行了开发和介绍;首先,分析了四旋翼无人机基本结构和飞行原理,并对其进行了动力学建模;其次,设计了对应的常规PID控制器和滑模控制器,并进行了Matlab仿真对比和分析;最后,展示了采用先进的基于模型的设计方法和代码自动等技术的半实物仿真实验平台,并详细介绍了其硬件和软件的总体架构和不同模块的配置;仿真结果表明所设计滑模控制器相比于PID控制器有更好的控制效果,并给出了其在半实物仿真平台上用来研究四旋翼无人机姿态控制的可行性。     

基于强化学习的四旋翼无人机控制律设计

目前四旋翼无人机大部分都采用经典控制方法进行控制律的设计,然而控制参数的选择和对被控对象数学模型的依赖一直是经典控制方法设计中需要克服的问题;针对此问题,采用了一种基于深度强化学习算法Deep Q Network的无人机控制律设计方法,以四旋翼姿态角和姿态角速率作为三层神经网络的输入数据,最终输出动作值函数,再根据贪婪策略进行动作的选取,通过与环境的不断交互,智能体根据奖惩信息来更新神经网络的权值,使得智能体朝着获得累积回报最大值的方向选取动作;仿真结果表明在经过强化学习训练之后,四旋翼姿态角能够快速准确地跟踪上参考指令的变化,证明了基于强化学习的四旋翼无人机控制律的可行性,从而避免了传统控制方法对控制参数的选择与控制模型的依赖。     

基于终端滑模的四旋翼飞行器非线性轨迹跟踪控制

考虑到四旋翼飞行器的传统内外环控制策略依赖时标分离假设,稳定性分析复杂,并且控制参数选取困难的缺点,提出了一种与传统内外环控制策略不同的轨迹跟踪控制器;首先将四旋翼飞行器数学模型进行相应的变换,以分解为高度、偏航角和纵横向三个级联的子系统,再使用终端滑模控制方法设计高度和偏航角子系统的控制器,使两个子系统的状态误差可以在有限时间内收敛到原点,之后基于变量非线性变换设计纵横向子系统的控制器,分析了闭环系统稳定性,证明了所设计的轨迹跟踪控制器可以保证闭环系统跟踪误差渐近稳定到原点,最后仿真实验的结果验证了所设计的控制器的有效性。     

基于RFID技术的四旋翼无人机轨迹跟踪控制系统设计

为促进四旋翼无人机的飞行自主性,增强无人监管情况下飞行器主机所具备的避障行进能力,设计基于RFID技术的四旋翼无人机轨迹跟踪控制系统;采用RFID标签识别技术,调制处理既定控制信号,利用标签识别协议,连接微型四旋翼轨迹控制器与内环姿态控制器,通过数据通信链路,提取轨迹跟踪控制所需的传输电子量,完成轨迹跟踪控制系统硬件设计;利用动力系统中的参数辨识策略,确定与轨迹姿态控制相关的物理规律标注,实现四旋翼无人机轨迹跟踪控制;实验结果表明,与机器视觉型控制系统相比,基于RFID技术的控制系统的SSI避障行进指标数值相对较高,全局最大值达到了 79％,四旋翼无人机滚转角平均值为85°,能够有效抑制四旋翼无人机滚转角的数值上升趋势,增强无人监管情况下飞行器主机避障行进能力.     

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。     

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.