A UKF-based estimation strategy for actuator fault detection of UASs

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating correspond- ing velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

State estimation of spiralling target using UKF with generalized sinusoidal target acceleration model

Hit-to-kill interception of high velocity spiraling target requires accurate state estimation of relative kinematic parameters describing spiralling motion. In this pa- per, spiraling target motion is captured by representing target acceleration through sinusoidal function in inertial frame. A nine state unscented Kalman filter (UKF) formulation is presented here with three relative positions, three relative velocities, spiraling frequency of target, inverse of ballistic coefficient and maneuvering coef-ficient. A key advantage of the target model presented here is that it is of generic nature and can capture spiraling as well as pure ballistic motions without any change of tuning parameters. Extensive Six-DOF simulation experiments, which includes a modified PN guidance and dynamic inversion based autopilot, show that near Hit-to-Kill performance can be obtained with noisy RF seeker measurements of gimbal angles, gimbal angle rates, range and range rate.

一种基于状态方差阵对角相似分解的UKF 算法

在强非线性和复杂性的系统下，普通对称采样UKF 算法存在稳定性问题。为此，本文提出了基于状态方差阵对角相似分解的UKF 算法来保证算法的状态方差阵半正定。同普通对称采样UKF 算法比较，该算法降低了对状态方差阵的正定性要求。仿真试验验证了该方法的有效性。

基于反馈线性化和自适应UKF的直升机航向控制

本文提出了一种新的自适应无色卡尔曼滤波(AUKF)方法.该方法以最小化新息方差阵的"真值"和估计值差的迹为指标函数，以MIT法则为自适应机制.通过自适应估计系统过程噪声方差阵，能够有效的补偿由于噪声先验知识不足和参数变化所引起的估计误差，提高UKF用于非线性系统状态、参数联合估计的性能.因而主动估计被进一步结合到反馈线性化中，使直升机航向动力学能够对内部不确定性有自适应性，也就是当时变、未知参数变化时达到鲁棒跟踪控制性能.并针对直升机航向动力学模型验证本方法的正确性；与常规UKF相比较显示该方法在收敛速度和估计准确程度方面的性能提高。

基于UKF滤波的自主移动机器人锂电池SOC估计

准确估计剩余电量(state of charge,SOC)关系到自主移动机器人(AMR)的生存与安全,是AMR研究中所面临的主要挑战之一。针对广义卡尔曼滤波估计SOC的不足,本文给出基于无色卡尔曼滤波(UKF)估计AMR锂电池SOC的新方法。通过试验对UKF和EKF进行了比较。试验验证了同样条件下,UKF比EKF具有更好的滤波估计精度。

基于UKF的移动机器人主动建模及模型自适应控制方法

利用基于无色卡尔曼滤波(UnscentedKalmanFilter,UKF)的状态和参数联合估计方法对移动机器人进行在线主动建模,基于该主动模型的逆动力学控制方法,实现了移动机器人对其自身不确定因素的自主性.在针对全方位移动机器人的仿真实验中,验证了UKF对时变的状态和参数的收敛性和跟踪能力,并给出了不确定界.基于主动建模的逆动力学控制方法与常值PID控制方法的比较结果,验证了该方法的有效性.

Aircraft dynamic navigation for unmanned aerial vehicles

This thesis describes the investigation of an Aircraft Dynamic Navigation (ADN) approach, which incorporates an Aircraft Dynamic Model (ADM) directly into the navigation filter of a fixed-wing aircraft or UAV. The result is a novel approach that offers both performance improvements and increased reliability during short-term GPS outages. This is important in allowing future UAVs to achieve routine, unconstrained, and safe operations in commercial environments. The primary contribution of this research is the formulation Unscented Kalman Filter (UKF) which incorporates a complex, non-linear, laterally and longitudinally coupled, ADM, and sensor suite consisting of a Global Positioning System (GPS) receiver, Inertial Measurement Unit (IMU), Electronic Compass (EC), and Air Data (AD) Pitot Static System.

Recursive actuator fault detection and diagnosis for emergency landing of UASs

This paper presents a practical recursive fault detection and diagnosis (FDD) scheme for online identification of actuator faults for unmanned aerial systems (UASs) based on the unscented Kalman filtering (UKF) method. The proposed FDD algorithm aims to monitor health status of actuators and provide indication of actuator faults with reliability, offering necessary information for the design of fault-tolerant flight control systems to compensate for side-effects and improve fail-safe capability when actuator faults occur. The fault detection is conducted by designing separate UKFs to detect aileron and elevator faults using a nonlinear six degree-of-freedom (DOF) UAS model. The fault diagnosis is achieved by isolating true faults by using the Bayesian Classifier (BC) method together with a decision criterion to avoid false alarms. High-fidelity simulations with and without measurement noise are conducted with practical constraints considered for typical actuator fault scenarios, and the proposed FDD exhibits consistent effectiveness in identifying occurrence of actuator faults, verifying its suitability for integration into the design of fault-tolerant flight control systems for emergency landing of UASs.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. ©2010 IEEE.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for sigma point placement, potentially causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. © 2011 Elsevier B.V.

基于MIT规则的自适应Unscented卡尔曼滤波及其在旋翼飞行机器人容错控制的应用

提出一种新颖的基于MIT规则的自适应Unscented卡尔曼滤波(Unscented Kalman filter,UKF)算法,用来进行参数以及状态的联合估计。针对旋翼飞行机器人执行器提出一种执行器健康因子(Actuator health coefficients,AHCs)的故障模型结构,应用自适应UKF对AHCs参数进行在线估计,将联合估计的状态以及故障参数引入基于模型的反馈线性化控制结构,组成完整的容错控制系统。提出的自适应UKF算法以及容错控制结构经过中科院沈阳自动化研究所ServoHeli-20旋翼无人智能平台数学模型进行仿真试验验证,效果良好。

基于主动建模的无人直升机增强LQR控制

为了解决无人直升机控制问题,通过把主动建模与LQR(Linear Quadratic Regulator)控制相结合,提出一种能补偿模型差的控制方法。该方法在悬停状态下,采用简化模型设计LQR控制器,并通过UKF(Un-scented-Kalman-Filter)在线估计简化模型与全状态模型的模型差,使用模型差作为补偿项对LQR控制增强。针对实际直升机动力学模型进行仿真,验证了基于UKF的估计和增强LQR控制的有效性。仿真实验结果证明,基于UKF的主动建模技术能够快速估计状态和参数变化,并且增强LQR控制能够使系统适应模型不确定性。

基于主动建模的自主移动机器人自适应容错控制

针对非线性自主移动机器人可能发生的驱动器故障,提出了一种新的自适应容错控制方法,即基于主动建模的逆动力学控制(IDC)方法.无色卡尔曼滤波(UKF)非线性估计方法用于对系统进行主动建模--状态和故障参数的在线联合估计,含有可调参数的逆动力学控制器用于根据UKF的估计结果进行控制策略的重构.仿真实验证明,具有主动建模的控制器能够有效地补偿系统的驱动器故障,使故障后的系统仍具有令人满意的性能。

面向自主移动机器人的主动建模及控制方法研究

自主能力是传统的工业机器人向今天更具智能的先进机器人发展的最重要的使能技术之一。而实时建模与自主适应控制则是实现机器人自主能力的最为关键的两种技术。本文以中科院沈阳自动化所自行研制的正交全方位轮式移动机器人为实验平台，深入系统的研究了自主机器人基于UKF的在线建模技术及控制方法。首先，深入地研究了UKF算法，并证明了其稳定性。阐述了U变换的基本原理，对U变换的精度进行了分析，阐述了随机过程有界的概念。介绍了标准UKF算法和平方根UKF算法。在此基础上，提出了UKF基本算法的稳定性条件，并给出了相应的证明过程，为UKF算法的应用奠定了理论基础。其次，研究了基于UKF的非线性系统实时状态和参数联合估计方法，提出了基于UKF主动建模的控制方法。在联合估计中，将系统中的时变参数与其真实状态联合，组成增广状态向量，再利用UKF对该增广状态进行估计，从而得到状态和参数的估计值。基于UKF主动建模的控制方法，是将上述状态/参数联合估计方法与逆动力学控制相结合，实现针对机器人自身参数不确定性的自适应控制。仿真结果表明，UKF算法对系统状态和参数的变化具有良好的估计性能，所提出的控制方法能够有效的克服时变参数对系统控制性能的影响。第三，研究了基于UKF的故障在线辨识与容错控制方法。提出了一种针对驱动器故障的参数化模型表达方法，利用UKF联合估计方法对故障参数进行在线估计。将实时故障参数估计与逆动力学控制相结合，构造出实时容错控制方法。在分析了全方位正交轮式移动机器人结构的基础上，离线地建立起该机器人的运动学和动力学参考模型，并以该离线参考模型为基础，进行了容错控制实验研究。实验结果表明在系统驱动器发生故障时，基于UKF主动建模的容错控制方法能够有效的提高系统的性能。最后，提出了两种具有噪声统计特性自适应能力的UKF算法，即基于MIT规则和基于KF估计的自适应UKF算法。基于MIT规则的自适应UKF算法，以新息方差的实际值与估计值的差为指标函数，采用MIT规则作为自适应机制，在线地估计系统噪声统计特性，以此提高UKF方法对噪声统计特性的自适应能力。基于KF估计的自适应UKF算法由两个并行的主、辅滤波器构成。辅助滤波器利用KF估计系统噪声方差，主滤波器利用该噪声方差的估计值，进一步预测估计系统的状态和参数。仿真表明在系统的噪声统计特性发生变化时，所提出的两种自适应UKF算法能够自动的调整自身参数，以弥补由于先验知识不足而产生的估计误差。之后，比较了两种自适应UKF算法在估计的准确程度、估计所用的CPU时间、调节的难易程度三个方面的性能，并给出了比较结果。