A state prediction scheme for discrete time nonlinear dynamic systems

A state prediction scheme is proposed for discrete time nonlinear dynamic systems with non-Gaussian disturbance and observation noises. This scheme is based upon quantization, multiple hypothesis testing, and dynamic programming. Dynamic models of the proposed scheme are as general as dynamic models of particle predictors, whereas the nonlinear models of the extended Kalman (EK) predictor are linear with respect to the disturbance and observation noises. The performance of the proposed scheme is compared with both the EK predictor and sampling importance resampling (SIR) particle predictor. Monte Carlo simulations have shown that the performances of the proposed scheme, EK predictor, and SIR particle predictor are all model-dependent, that is, one performs better than the others for a given example. Some examples, for which the proposed scheme performs better than the others do, are also given in the paper.     

Fault detection and prediction scheme for nonlinear stochastic distribution systems

Model-based fault detection technique has a broad range of applications because of the small change to the system when the system state is known to be available and the low cost. For nonlinear stochastic distribution systems containing uncertain disturbance term, a model-based fault detection and failure time prediction scheme is proposed in this paper, and observers are designed to detect whether the incipient fault has occurred in the system. The residual is obtained by comparing the output of the actual system with the output of the observer. When the residual exceeds the threshold value obtained by derivation, it is determined that the fault has occurred in the system. The fault size can then be estimated in real time and used to determine the time to failure (TTF) or the remaining useful life of the system. The TTF of the system is obtained by comparing the magnitude of the current system fault with the fault threshold. Finally, the feasibility of the presented fault detection scheme is proved by the Lyapunov stability theory and the validity of the scheme is proved by computer simulation.     

Sufficient stability conditions for the weighted minimum uncertainty prediction controller

Sufficient BIBO-stability conditions are provided for the weighted minimum uncertainty prediction controller. This controller minimizes a cost functional which penalizes the predicted output uncertainty, the control effort, and the tracking error. The BIBO-stability is addressed by examining the worst case time-variant interval matrix representation of the closed loop system.     

On the convergence of an extended state observer for nonlinear systems with uncertainty

The extended state observer first proposed by Jingqing Han in [J.Q. Han, A class of extended state observers for uncertain systems, Control Decis. 10 (1) (1995) 85-88 (in Chinese)] is the key link toward the active disturbance rejection control that is taking off as a technology after numerous successful applications in engineering. Unfortunately, there is no rigorous proof of convergence to date. In this paper, we attempt to tackle this long unsolved extraordinary problem. The main idea is to transform the error equation of objective system with its extended state observer into a asymptotical stable system with a small disturbance, for which the effect of total disturbance error is eliminated by the high-gain.     

基于随机摄动粒子滤波器的故障预报算法

针对粒子滤波算法在故障预报中的大计算量和粒子退化问题,提出一种基于随机摄动粒子滤波器的故障预报算法.当粒子退化严重时,对粒子用随机摄动方式进行再采样,一方面可改进样本的多样性,缓解粒子退化;另一方面可缩短再采样时间,减少计算量,从而提高粒子滤波算法的跟踪能力.仿真结果表明该算法可行,能及时准确地对系统故障进行预报.     

强跟踪平方根容积卡尔曼滤波和 自回归模型融合的故障预测

为了解决非线性系统中不可测量参数的预测问题,提出一种带有次优渐消因子的强跟踪平方根容积卡尔曼滤波(STSCKF)和自回归(AR)模型相结合的故障预测方法.利用AR模型时间序列预测法预测未来时刻的测量值,将预测的测量值作为STSCKF的测量变量,从而将预测问题转化为滤波估计问题.STSCKF通过在预测误差方差阵的均方根中引入渐消因子调节滤波过程中的增益矩阵,克服了故障参数变化函数未知情况下普通SCKF跟踪故障参数缓慢甚至失效的局限性,使得STSCKF能较好地预测故障参数的发展趋势.连续搅拌反应釜(CSTR)仿真结果表明,STSCKF的预测精度高于普通SCKF和强跟踪无迹卡尔曼滤波(STUKF),验证了方法的有效性.     

非线性系统的故障诊断技术

文中对非线性系统的故障诊断方面问题给予了归纳总结,指出了基于数学模型方法,基于信号处理方法和基于知识的方法在实现非线性系统故障诊断的基本思想,并进一步指出了各各非线性系统故障诊断方法及可能的发展方向。     

Fault diagnosis of networked control systems

Networked control systems (NCS) are feedback systems closed through data networks. NCS have many advantages compared with traditional systems; however, the network-induced delay and other characteristics of data networks may degrade the performance of feedback systems designed without taking the network into account. Supported by the National Nature Science Foundation of China, we studied the fault diagnosis and fault-tolerant control theory for NCS in recent years. This paper summarizes our main ideas and results on fault diagnosis of NCS, including the fundamentals of fault diagnosis for NCS with information-scheduling, fault diagnosis approaches based on the simplified time-delay system models, and the quasi T-S fuzzy model and fault diagnosis for linear and nonlinear NCS with long delay.     

Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control

The performance of modern control methods, such as model predictive control, depends significantly on the accuracy of the system model. In practice, however, stochastic uncertainties are commonly present, resulting from inaccuracies in the modeling or external disturbances, which can have a negative impact on the control performance. This article reviews the literature on methods for predicting probabilistic uncertainties for nonlinear systems. Since a precise prediction of probability density functions comes along with a high computational effort in the nonlinear case, the focus of this article is on approximating methods, which are of particular relevance in control engineering practice. The methods are classified with respect to their approximation type and with respect to the assumptions about the input and output distribution. Furthermore, the application of these prediction methods to stochastic model predictive control is discussed including a literature review for nonlinear systems. Finally, the most important probabilistic prediction methods are evaluated numerically. For this purpose, the estimation accuracies of the methods are investigated first and the performance of a stochastic model predictive controller with different prediction methods is examined subsequently using multiple nonlinear systems, including the dynamics of an autonomous vehicle.     

基于H∞滤波器的非线性摄动时滞系统的故障诊断

针对一类非线性摄动时滞系统，基于H∞滤波器技术，探讨了系统的故障诊断问题．首先引入参考模型，通过构建故障观测器，形成能反映系统故障的广义残差模型；然后通过基于线性矩阵不等式（LMI）的方法，将故障诊断问题转化为系统鲁棒稳定性分析问题，并给出该问题解存在的LMI条件和求法．该方法既提高了故障观测器对残差的敏感性，又有效地抑制了干扰，提高了故障检测的效果．仿真结果表明了该算法的有效性．     

基于故障跟踪估计器的非线性时滞系统故障诊断

提出一种可有效检测和估计一类非线性时滞系统故障的故障跟踪估计器.根据预测控制和迭代学习控制的思想,在所选取的优化时域长度内,通过迭代算法调节故障跟踪估计器中的可调参数,使之逼近系统中实际发生的故障.与以往基于观测器的故障诊断方法不同的是,故障跟踪估计器可同时检测和估计系统中发生的故障,而且针对不同类型的故障亦有很好的适应性.仿真结果表明了所提出算法的可行性和有效性.     

Fault prognosis of discrete event systems: An overview

Fault prognosis of discrete event systems (DES) is an active field of research and has become important due to the high demand on systems’ safety and reliability. The aim of this paper is to provide the state-of-the-art on fault prognosis of DES, as well as quick directions regarding many aspects of the research on this topic. This overview was carried out using a systematic approach, which allowed us to find and select papers with minimum bias. In total, 74 papers were selected and analyzed in this overview. A classification and an analysis of these papers are made regarding the modeling formalism and decision structure of prognosis of DES. Specific notions of prognosis are also presented. A survey on these papers was performed, regarding practical applications and related problems of prognosis. Results show that most papers present solutions based on languages & automata (56), and most papers consider the centralized decision structure (45). Also, we have found 17 different papers with examples of practical applications of prognosis of DES. We have identified research gaps, such as applications of distributed prognosis and distributed prognosis of Petri nets. Finally, the research activity on prognosis of DES is growing 16.14% by year on average, accordingly to the number of published papers on this topic between 2006 and 2020.     

装备故障模糊预测系统研究

以模糊综合评判理论为基础,建立了一种动态的故障模糊预测模型,结合Adams仿真平台,探讨了故障模糊预测系统设计方法,给出了故障预测的推理过程。     

Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation

This paper presents a fault detection and isolation (FDI) scheme for a class of Lipschitz nonlinear systems with nonlinear and unstructured modeling uncertainty. This significantly extends previous results by considering a more general class of system nonlinearities which are modeled as functions of the system input and partially measurable state variables. A new FDI method is developed using adaptive estimation techniques. The FDI architecture consists of a fault detection estimator and a bank of fault isolation estimators. The fault detectability and isolability conditions, characterizing the class of faults that are detectable and isolable by the proposed scheme, are rigorously established. The fault isolability condition is derived via the so-called fault mismatch functions, which are defined to characterize the mutual difference between pairs of possible faults. A simulation example of a single-link flexible joint robot is used to illustrate the effectiveness of the proposed scheme.     

动态系统的故障预报技术

首先介绍了动态系统故障预报技术的发展状况,然后分三类介绍了目前已有的各种故障预报技术,并讨论了各种方法的优缺点,随后介绍了一些典型的应用例子,最后对这一领域的发展趋势进行了探讨.     

基于案例推理的竖炉故障预报系统

为降低竖望炉焙烧过程的故障发生率,基于故障机理的分析,将过程参量预报与案例推理技术相集成,提出了竖炉焙烧过程的智能故障预报方法.参量量预报模型对不易在线连续测量但能反映故障征兆的关键工艺参数进行实时预报,在此基础上,采用案例推理技术对焙烧过程进行全面分析并给出一些典型故障发生的概率和操作指导.将所建立的故障预报系统成功应用于竖炉焙烧过程的生产实际中,故障发生率明显降低,取得了显著应用成效.     

Fault estimation and prediction for nonlinear stochastic system with intermittent observations

This paper is concerned with the fault estimation and prediction problems for a class of nonlinear stochastic systems with intermittent observations. Based on the extended Kalman filter and Kalman filter, the fault and state are simultaneously estimated, and then, it is extended to the case of intermittent observations. Meanwhile, the boundedness of the estimation error is also discussed. Once the fault is detected, the parameters of each fault are identified by the linear regression method. Then, the future fault signal can be predicted by the parameters of the fault. The effectiveness of the proposed algorithm is verified by the simulation of the 3‐tank system.     

Fault diagnosis of nonlinear systems using structured augmented state models

This paper presents an internal model approach for modeling and diagnostic functionality design for nonlinear systems operating subject to single- and multiple-faults. We therefore provide the framework of structured augmented state models. Fault characteristics are considered to be generated by dynamical exosystems that are switched via equality constraints to overcome the augmented state observability limiting the number of diagnosable faults. Based on the proposed model, the fault diagnosis problem is specified as an optimal hybrid augmented state estimation problem. Sub-optimal solutions are motivated and exemplified for the fault diagnosis of the well-known three-tank benchmark. As the considered class of fault diagnosis problems is large, the suggested approach is not only of theoretical interest but also of high practical relevance.