基于改进自抗扰的四旋翼无人机轨迹跟踪控制

四旋翼无人机具有较强的非线性特性,容易受到内外部不确定性扰动的影响,因此精准的轨迹跟踪控制成为挑战。本文针对上述问题提出了一种基于改进自抗扰控制和模型预测控制结合的分层控制架构以提高系统的鲁棒性及跟踪精度。首先,基于非线性自抗扰控制设计了外环位置控制器,同时针对自抗扰控制存在的相位滞后问题,设计了相位补偿器;然后基于模型预测控制设计了内环姿态控制器。本文所采用的控制方法不依赖于精确的数学模型,能够在满足系统约束的条件下对给定的轨迹进行精确的跟踪,具有较强的抗干扰能力。通过典型工况的仿真实验表明本文所提控制方法在轨迹跟踪精度上较传统自抗扰控制提升了40%以上,且具有较强的鲁棒性。     

基于四旋翼无人机的输电线路巡检实时自主跟踪系统设计

本文基于四旋翼无人机的输电线路巡检提出一种硬件和软件的实时自主跟踪系统的设计.为了提高无人机的检测效率,需要对实时数据处理传输线进行检测和跟踪.本文提出无人机的硬件结构设计,软件设计结构,以及卡尔曼滤波器在激光雷达上面的数据处理.并进行了飞行实验验证了系统的可行性.     

基于MCU的四旋翼无人机智能电源系统设计

本文介绍了一种基于MCU的四旋翼无人机智能电源管理系统设计和实现方案。本系统以STM32 MCU为控制核心,以TI公司的降压控制器LM5117和CSD18532KCSMOS为核心器件,由滤波模块、稳压模块、电压反馈模块、AD控制模块,显示模块组成。利用LM5117的DC-DC功能和反馈模块,系统不但实现了动力系统要求的恒压恒流功能,此外还具有过流保护功能以及负载识别能力;通过抑制电源纹波的产生,满足了控制系统的低噪声纹波电压的要求,另外系统进行了抗干扰设计。     

基于微型四旋翼无人机的智能导航系统

为了实现四旋翼无人机的自主飞行,设计了该智能导航控制系统.飞行控制器采用陀螺仪与三轴加速度传感器相结合的方式检测飞行器姿态,通过ATMEGA644芯片分别控制4个电机驱动模块调速来实现飞行姿态的改变.导航系统通过ARM7控制GPS模块与电子罗盘,获取飞行器的实时位置,并利用软件滤波的方式提高了定位精度.设计了上位机控制...     

基于改进双闭环滑模算法的飞行器轨迹跟踪研究

针对飞行器运动轨迹受外界气流干扰和模型参数不确定性导致复杂空间运动轨迹跟踪精度不足的问题,提出了一种改进的双闭环滑模控制算法提高飞行器轨迹跟踪的精度。该算法中外环位置控制器采用自适应率抑制外界干扰影响,内环姿态控制器利用切换函数自适应模糊器抑制未知干扰力矩和抖振的影响。利用Lyapunov理论验证双闭环控制算法具有全局渐近稳定性。仿真结果表明,改进的双闭环控制算法的姿态角跟踪误差精度由0.02提高到了0.0005,该算法对外界干扰具有较强的鲁棒性,同时能够实现高精度的轨迹跟踪。     

基于四旋翼无人机的二次屏柜巡检系统

随着人们对于电力的需求量越来越大,相应的电力巡检的要求也越来越高。文中设计了基于四旋翼无人机的二次屏柜巡检系统。为解决无人机在室内GPS拒止问题,文中通过AprilTag算法配合传感器高度信息,实现无人机室内飞行路径规划功能、姿态矫正、自主飞行;在机载树莓派内存入任务程序,该程序将利用飞控和传感器实现对任务地点二维码的寻找和判断,并在达到指定点后执行拍摄任务;通过局域网将采集的图像传输到地面服务器,并输入到预先训练好的CNN网络模型中进行异常检测;依据识别结果判断二次屏柜是否工作正常,并将检测结果上传到显示平台。最终项目在贵州铜仁变电站室内二次屏柜现场测试成功。     

基于四旋翼无人机的二次屏柜巡检系统

随着人们对于电力的需求量越来越大,相应的电力巡检的要求也越来越高。文中设计了基于四旋翼无人机的二次屏柜巡检系统。为解决无人机在室内GPS拒止问题,文中通过AprilTag算法配合传感器高度信息,实现无人机室内飞行路径规划功能、姿态矫正、自主飞行;在机载树莓派内存入任务程序,该程序将利用飞控和传感器实现对任务地点二维码的寻找和判断,并在达到指定点后执行拍摄任务;通过局域网将采集的图像传输到地面服务器,并输入到预先训练好的CNN网络模型中进行异常检测;依据识别结果判断二次屏柜是否工作正常,并将检测结果上传到显示平台。最终项目在贵州铜仁变电站室内二次屏柜现场测试成功。     

基于Backstepping时变反馈和PID控制的移动机器人实时轨迹跟踪控制

根据机器人的运动学模型，采用分层控制的思想将移动机器人的轨迹跟踪控制分为两部分：轨迹跟踪控制器和机器人速度PID控制器。基于Backstepping时变状态反馈方法和Lyapunov理论，引入具有双曲正切特性的虚拟反馈量，提出一种移动机器人全局轨迹跟踪算法：采用PID速度控制器以满足机器人驱动电机实时调速要求。考虑到机器人的动力学约束，引入受限策略以保证其运动平滑。在基于DSP的两轮驱动移动机器人上对算法进行了实时轨迹跟踪试验，取得了满意的控制效果。     

四旋翼无人机在输电线路巡视中的应用

四旋翼无人机在输电线路中巡视作业,可有效代替人工巡视,具有机动灵活、安全稳定和快捷高效的特点,可以对输电线路本体缺陷、通道隐患进行快速、精确核查,可以在各种复杂地形及灾害情况下安全、高效地执行应急任务,大大提高输电线路巡视、维护质量和技术水平。     

Quaternion-based adaptive control for trajectory tracking of quadrotor unmanned aerial vehicles

The problem of trajectory tracking of aerial vehicles with four rotors and dynamic parameter uncertainties is addressed in this article. By exploiting the cascade structure of the translational and attitude dynamics of the quadrotor, a nonlinear hierarchical adaptive control is proposed. The attitude control law is designed based on the unit quaternion, therefore, the singularities of the Euler angles and angle-axis representation are avoided. Instead of relying on the stability properties of time-varying cascade systems, the stability of the whole closed-loop system is proven by an appropriate Lyapunov function. In addition, a novel method to compute the desired orientation based on the desired Cartesian trajectories and position control inputs is proposed. A set of simulations results are presented to assess the performance of the proposed adaptive controller.     

Adaptive robust output feedback control for attitude tracking of quadrotor unmanned aerial vehicles

This work proposes a new adaptive robust output feedback control method for attitude reference tracking of a quadrotor unmanned aerial vehicle without using the angular velocity measurements. By using the K-filters well known in the adaptive control community, the necessity of velocity measurements or estimating is avoided. The attitude system model is transformed into a second-order model where the angular velocity measurements are not involved. However, the model includes mismatched uncertainties which should be estimated and compensated by the disturbance observers (DOBs). The controller is designed in a backstepping manner, and the dynamic surface technique is adopted to avoid the explosion of the controller complexity. For each Euler angle axis, the prescribed performance control technique is adopted to ensure a prescribed performance, the lumped disturbance is compensated by a DOB, and furthermore an adaptive law is introduced to adaptively update the corresponding uncertain inertia parameter which affects the control performance significantly. The control performance of the overall control system is analyzed rigorously from the viewpoint of input-to-state practical stability. In addition, it is shown how the adaptive laws contribute to improving the control performance. And simulation examples are provided to demonstrate the performance of the proposed method.     

基于加速度变噪声EKF的无人机姿态融合算法

针对传统无人机姿态解算方法过程复杂、计算量大、动态性能差的缺点,建立无人机姿态模型;采用陀螺仪对加速度计直接进行滤波的方法,设计出新的基于扩展kalman滤波的加速度滤波器;并且考虑到无人机非重力加速度的影响,对常规kalman滤波器进行了变噪声的改进。利用STM32微控制器和MEMS惯性单元搭建硬件平台进行对比实验。结果表明:在168 MHz时钟频率下,一次传感器数据读取和姿态解算总共耗时3.27 ms,数据更新率可达100 Hz。新算法飞行动态误差小于1°,而传统四元数法动态误差为2°左右;变噪声处理后静态瞬时偏差由4°降到1°。说明新算法的抗震效果和解算精度更好,可以为无人机自主飞行提供更准确的姿态信息。     

Integration of multiple model based fault detection and nonlinear model predictive fault‐tolerant control

In this paper, we present a new fault‐tolerant control system for a class of nonlinear systems with input constraints. Because of many important factors that stabilize a nonlinear model predictive controller, it can be used as a powerful controller in the event of fault occurrence. So, the reconfigurable controller is designed based on the quasi‐infinite model predictive control (QIMPC) approach as a fault‐tolerant approach. On the other hand, a fault detection and diagnosis (FDD) system is designed based on the multiple model method. The bank of extended Kalman filters (EKFs) is used to detect the predefined actuator fault and estimate the unknown parameters of a fault. When a fault is detected, the proposed FDD information is used to correct the model of the faulty system recursively and reconfigure the controller. Delay on FDD decision may lead to performance degradation or even instability for some systems. The timely proposed FDD approach will preserve stability. Moreover, a framework is presented to ensure stability when a fault occurs. The effectiveness of this method is demonstrated, in comparison with conventional nonlinear model predictive control, by two practical examples. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于模糊纯追踪模型的隧道台车路径追踪控制

针对目前隧道台车行走以人工操作为主要方式,为了提高其智能化程度和施工效率,提出了一种基于模糊纯追踪的台车路径跟踪控制方法,将纯追踪算法和模糊控制相结合,改变了传统台车以轮轨式或轮胎式手动操作行走的方式。建立了台车抽象数学模型,以此确定台车跟踪路径过程中的转向半径,进行了跟踪直线和变道曲线的仿真实验,并搭建试验小车对纯追踪模型和模糊纯追踪模型进行实验,将实验结果进行了对比。结果表明本文提出控制系统在直线路径追踪过程中最大横向误差不超过5cm,且在跟踪路径x=7.3m处达到收敛,趋于稳定后的平均绝对误差为0.004m。仿真和实验结果均验证了本文所提控制系统在提高收敛速度和稳定性方面是有效的。     

基于Mahony-EKF的无人机姿态解算算法

针对微惯性测量单元精度低和传统姿态解算方法误差较大,提出一种Mahony和扩展卡尔曼滤波(EKF)融合的姿态解算算法。首先通过Mahony滤波器融合陀螺仪、加速度计和磁力计数据,解算得到初步姿态四元数。再以Mahony滤波器的姿态四元数作为EKF的量测值,根据非重力加速度的大小,自适应正相关调节量测噪声协方差矩阵;根据陀螺仪测量的角速度信息建立EKF状态方程。最终经过EKF滤波后,获取无人机姿态的估计。经过仿真实验验证,融合算法解算静态姿态角误差小于0.1°,解算动态姿态角误差小于1°,均优于互补滤波算法和改进EKF算法。融合算法能有效抑制陀螺仪漂移误差,滤除加速度计测量值混有的高频噪声和抑制非重力加速度的干扰,提高姿态解算精度。     

基于VMD和MPC的电动汽车-火电机组联合调频控制

合理的调频指令分配策略以及有效的指令跟踪控制方法是利用集群电动汽车(aggregate electric vehicle, AEV)联合火电机组开展调频控制、改善调频质量、提高调频经济性的关键。基于此,文中提出基于变分模态分解(variational mode decomposition, VMD)和双层模型预测控制(model predictive control, MPC)的AEV参与系统调频控制策略。首先,设计AEV联合传统火电机组的频率协调优化控制结构,建立火电机组及负荷频率控制模型,同时将AEV转化为虚拟调频单元,构建在AEV参与下系统单区域多机组负荷频率控制模型;然后,利用VMD将电网调频指令信号分解为含不同频率成分的本征模态函数,整合高频分量作为AEV的调频指令,低频分量作为火电机组群的调频指令,并通过双层MPC分别在AEV和火电机组群内部实现调频指令的优化再分配及跟踪控制;最后,对所提控制策略进行仿真验证。结果表明所提控制策略可实现对系统频率的有效调节,且兼顾了调频的经济性和动态性能。     

Improved PMSM model considering flux characteristics for model predictive‐based current control

Model predictive‐based current control, which was proposed in our previous study, offers better current response performance by employing mathematical models of an inverter and a permanent magnet synchronous motor (PMSM). The performance of this kind of approach depends on the predictive model. From a more practical point of view, current behavior in the steady state should be improved. In our previous model predictive‐based current control, the inverter model was refined by taking the dead‐time into consideration. The use of the refined inverter model reduces the current offset in the control. However, the PMSM model was not investigated. This paper proposes a more appropriate PMSM model for model predictive‐based current control in order to improve the current prediction in the steady state. For the purpose, we incorporate more detailed magnetic flux characteristics instead of average characteristics into the improved PMSM model. Specifically, in the improved PMSM model, the inductance of the PMSM is divided into transient and steady‐state parts on the basis of magnetic saturation. The effectiveness of the improved model in the model predictive‐based current control is demonstrated through simulations and experiments. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.