考虑传感器故障的导弹姿态控制系统主动容错控制研究

针对导弹姿态控制系统惯性传感器故障,提出了基于信号重构的主动容错控制方法.分别利用基于梯形算法的数值积分器和有限时间收敛微分器对姿态角信号和角速率信号进行重构,当在线诊断出姿态角或角速率传感器故障时,以重构信号代替故障信号进行反馈控制来实现系统的主动容错控制.在建立导弹姿态控制系统模型并采用次最优控制方法设计输出反馈控制器的基础上,对所设计的主动容错控制方法进行仿真,仿真结果表明该方法是有效的.     

一类非线性系统的故障重构与容错控制

针对一类满足Lipschitz条件的仿射非线性系统,提出一种执行器故障重构与容错控制方法。通过非奇异变化矩阵对系统进行降阶,设计出滑模故障重构观测器,优化滑模策略,使滑模故障重构观测器渐进估计系统的状态,并给出稳定性分析。运用等价输出控制方法直接获取故障信息,实现执行器故障的检测与重构。设计出主动容错控制器,通过补偿控制,完成执行器故障的容错控制。最后通过数值仿真验证了方法的可行性与有效性。     

考虑多传感器故障的可重构机械臂主动取代分散容错控制

针对可重构机械臂系统位置传感器和速度传感器多故障, 提出一种主动取代分散容错控制方法. 基于可重构机械臂的模块化属性, 设计正常工作模式下的分散神经网络控制器. 利用微分同胚原理将子系统结构进行非线性变换, 将传感器故障转化成伪执行器故障, 设计分散滑模观测器以对多传感器故障进行实时检测, 并利用其输出信号取代故障传感器信号, 实现了多传感器故障情形下可重构机械臂的主动容错控制. 仿真结果表明了所设计的容错控制方法的有效性.     

可重构机械臂反演时延分散容错控制

针对存在模型参数不确定性的可重构机械臂系统执行器故障,提出一种基于反演设计与时延技术相结合的容错控制方法.该方法利用反演设计的基本思想,通过神经网络补偿子系统动力学模型中的参数不确定项和关联项.利用时延控制的逼近能力来补偿执行器的故障,使得故障发生时能及时实现容错控制.该方法具有不需要在线进行故障诊断的特点,仿真结果表明了所提出控制方法的有效性.     

一种面向多核的可重构容错方法

随着二代导航、载人航天、深空探测等空间应用对星载电子产品的低功耗和抗辐射容错能力提出更高的需求,传统多机冗余设计星载计算机面临着亟需进行设计升级换代。将可重构技术应用到多核片上系统的设计中,提出了一种基于动态可重构的容错体系结构,在硬件层提高系统的容错能力和扩展性对未来空间工程应用具有重要意义。首先介绍了多核片上系统和可重构技术的基本概念,简要分析了国际宇航可重构系统的研究案例。随后提出了一种基于动态可重构的容错体系结构,即通过基于系统降级的重构策略来实现系统级容错。在方案验证环节,采用LEON3作为处理单元,对容错模块功能进行了仿真验证。仿真结果表明,容错控制满足预期的设计需求。最后对后续工作做了简要规划,并对可重构容错方法设计进行了总结。     

基于信号重构的可重构机械臂主动分散容错控制

针对可重构机械臂系统传感器故障,提出一种基于信号重构的主动分散容错控制方法. 基于可重构机械臂系统模块化属性,采用自适应模糊分散控制系统实现正常工作模式时模块关节的轨迹跟踪控制. 当在线检测出位置或速度传感器故障时,分别采用数值积分器或微分跟踪器重构相应信号,并以之代替故障信号进行反馈实现系统的主动容错控制. 此方法充分利用了冗余信息,避免了故障关节控制性能的下降对其他关节的影响. 数值仿真结果验证了所提出容错控制方法的有效性.     

基于神经网络模型辨识的串级控制系统传感器容错技术研究

本文根据控制律重构设计的思想，提出了串级控制系统主动容错方案。在工业过程中串级控制系统发生传感器失效故障的情况下，利用人工神经网络在线辨识被控对象模型，在故障诊断系统输出的故障信息驱动下，实现控制律在线重构设计，使工业过程在重构的控制系统控制下稳定工作，保证性能指标。最后，以一个串级控制回路为对象进行了仿真验证，结果表明上述方案达到比较满蔷的容错控制效果。     

动态系统的故障检测与容错控制

本文详尽地论述了各种容错控制方法,讨论了动态系统的各种故障检测技术及系统重构技术。     

风能转换系统执行器故障重构与容错控制

针对风能转换系统中执行器故障,论文提出了一种新型的主动容错控制策略.设计滑模故障观测器,实时动态采集执行器故障前后数据信息,对执行器故障进行重构,达到故障诊断的目的.通过补偿控制,保证了滑模控制器对风能转换系统的可靠控制输入,以达到对执行器故障主动容错的功能.仿真结果表明,滑模故障观测器模块能够实时精确地重构风能转换系统执行器故障,主动补偿容错控制器在不影响风能转换系统动态性能的情况下,仍能实现系统的最大风能的捕获.     

容错控制系统动态稳定的控制率重构

针对反馈系统中的传感器故障,基于非线性优化提出两种动态稳定的容错控制律重构设计方法。算例表明本文方法的有效性。     

Condition monitoring and fault diagnostics for hydropower plants

This paper presents a condition monitoring and fault diagnostics (CMFD) system for hydropower plants (HPP). CMFD is based on the concept of industrial product-service systems (IPS2), in which the customer, turbine supplier, and maintenance service provider are the IPS2 stakeholders. The proposed CMFD consists of signal acquisition, data transfer to the virtual diagnostics center (VDC) and fault diagnostics. A support vector machine (SVM) classifier has been used for fault diagnostics. CMFD has been implemented on an HPP with three Kaplan units. A signal acquisition system for CMFD consists of data acquisition from a unit control system and a supplementary system for high-frequency data acquisition. The implemented SVM method exhibits high training accuracy and thus enables adequate fault diagnostics. The data are analyzed in the VDC, which allows all stakeholders access to diagnostic information from anywhere at any time. Based on this information, the service providers can establish condition-based maintenance and offer operational support. Furthermore, through the VDC, cooperation between the stakeholders can be achieved; thus, better maintenance scheduling is possible, which will be reflected in higher system availability.     

倾转式三旋翼无人机的自适应鲁棒容错控制

本文针对受到外界未知扰动和模型不确定性影响的倾转式三旋翼无人机,研究了其在尾部舵机发生堵塞故障时的容错控制问题.通过对倾转式三旋翼无人机姿态动力学特性的分析,将尾部舵机堵塞故障加入到力矩解算方程中.基于自适应反步法和非奇异终端滑模控制,提出了一种不需要故障诊断的鲁棒容错控制设计.利用基于Lyapunov的分析方法证明了闭环系统的稳定性,以及姿态误差的渐近收敛性质.通过在三旋翼无人机半实物仿真平台的实时实验以及与滑模控制器的对比,验证了该算法在无人机尾部舵机发生堵塞故障时,对姿态运动具有较好的控制效果.     

Fault Diagnosis and Sliding Mode Fault Tolerant Control for Non‐Gaussian Stochastic Distribution Control Systems Using T‐s Fuzzy Model

For the non‐Gaussian stochastic distribution control system using Takagi‐Sugeno fuzzy model, a new fault diagnosis and sliding mode fault tolerant control algorithm is presented. First, a new adaptive fault diagnosis algorithm is adopted to diagnose the fault that occurred in the system, and the observation error system is proven to be uniformly bounded. Second, the sliding mode control algorithm is used to reconfigure the controller, based on the fault estimation information. The post‐fault probability density function can still track the given distribution, leading to fault tolerant control of non‐Gaussian stochastic distribution control systems using Takagi‐Sugeno fuzzy model. Finally, simulation results show the effectiveness of the proposed method.     

Fault‐Tolerant Control Based on Augmented State Estimator and PDF

A new fault‐tolerant control based on augmented state estimator and probability density function (PDF) is proposed for a stochastic distribution system (SDS) with time‐delay and additive fault. First, a system model based on a PDF with the additive fault is constructed by using square‐root rational B‐spline neural networks. Second, an augmented system is obtained by converting the additive fault as an auxiliary state variable. In this framework, a robust augmented state estimator is designed to estimate the original states and the additive fault simultaneously. Then, based on the obtained estimation of fault, a delay‐dependent fault‐tolerant control is designed to compensate the fault. Finally, the numerical simulations show the effectiveness of the proposed method.     

运行控制系统集中式容错控制方法

本文针对运行控制系统,建立了运行优化控制过程的双层结构模型.在此基础上,通过建立相应的自适应故障诊断算法,提出了保证在系统有故障和干扰时仍能渐近优化指标的集中式容错控制方法,利用李雅普诺夫稳定性理论分析了自适应故障诊断算法的构建.已证明:该方法通过调整已优化的设定值来保证在回路控制层出现故障时整个运行控制仍可收敛到其原有的优化控制效果.该方法属于非完备容错控制,仿真结果验证了所提方法的有效性.     

Novel MPC‐Based Fault Tolerant Tracking Control Against Sensor Faults

The problem of active fault‐tolerant tracking control with control input and system output constraints is studied for a class of discrete‐time systems subject to sensor faults. A time‐varying fault‐tolerant observer is first developed to estimate the real system state from the faulty sensor output and control input signals. Then by using the estimated state at each time step, a model predictive control (MPC)‐based fault‐tolerant tracking control scheme is presented to guarantee the desired tracking performance and the given input and output constraints on the faulty system. In comparison with many existing fault‐tolerant MPC methods, its main contribution is that the proposed state estimator is designed by the simple and online numerical computation to tolerate the possible sensor faults, so that the regular MPC algorithm without fault information can be adopted for the online calculation of fault‐tolerant control signal. The potential recursive infeasibility and computational complexity due to the faults are avoided in the scheme. Additionally, the closed‐loop stability of the post‐fault system is discussed. Simulative results of an electric throttle control system verify the effectiveness of the proposed method.     

推力矢量飞机鲁棒故障检测与辨识和 指令滤波容错控制系统设计

针对推力矢量飞机(TVA)在超机动飞行中的舵面故障、执行器故障、参数摄动和外界干扰等问题,提出了一种鲁棒故障检测与辨识和指令滤波容错控制(RFDI–CFFTC)系统设计方法.首先针对TVA故障模型提出了一种基于多观测器的RFDI机制,通过引入自适应律和观测器来补偿参数摄动和外界干扰的影响,实现对舵面故障和执行器故障的准确检测与辨识,同时准确估计故障程度;然后在RFDI的基础上,设计了CFFTC容错控制律,实现包容舵面故障、执行器故障、参数摄动和外界干扰的TVA容错控制,同时改善了传统反步法中的“微分爆炸”问题;最后对RFDI–CFFTC系统的稳定性进行了证明.MATLAB/Simulink仿真验证了本文方法的有效性.     

具有传输时延的网络控制系统故障估计与调节

In this paper, a method of fault estimation and fault tolerant control for networked control system (NCS) with transfer delay and process noise is presented. First, the networked control system is modeled as a multiple-input-multiple-output (MIMO) discrete-time system with transfer delays, process noise, and model uncertainties. Under this model and under some conditions, a fault estimation method is proposed to estimate the system faults. On the basis of the information on fault estimation and the sliding mode control theory, a fault tolerant controller is designed to recover the system performance. Finally, simulation results are used to verify the efficiency of the method.     

基于有理平方根逼近的非高斯随机分布系统的故障诊断和容错控制

随机分布系统指的是输入为常规向量而输出为系统输出的概率密度函数所描述的一类随机系统．该类系统控制算法的目标是选择一个控制输入使得系统的实际输出概率密度函数尽可能跟踪一个事先给定的概率密度函数．本文对采用有理平方根B样条逼近其输出概率密度函数的非高斯动态随机分布系统,提出了一种基于非线性自适应观测器的故障诊断方法．该方法可快速有效地诊断出非高斯随机分布系统故障．通过对故障系统的重组,使故障后系统的输出概率密度函数仍能跟踪给定的分布,实现了该随机系统的容错控制,提高了随机系统的可靠性．