双容液位系统鲁棒自适应容错控制

针对双容液位控制系统的泄漏等故障,通过线性化建模,研究了鲁棒自适应主动容错控制问题.首先在系统无故障正常运行情形,考虑建模误差和外界干扰等不确定性,利用不确定性上界自适应估计,设计了鲁棒自适应控制器.与此同时,对系统进行故障监控,设计了故障诊断滤波器,并利用对不确定性上界的估计终值提出了一种新的故障检测算法,进一步基于神经网络故障逼近,研究了一种修正控制律的自适应鲁棒容错控制器设计方法,该控制器通过补偿故障所带来的影响使闭环系统最终一致有界稳定.最后,通过仿真试验,验证了提出的方法的有效性.     

不确定奇异时滞系统的鲁棒H∞故障诊断滤波器设计

研究一类受参数不确定性和干扰影响的奇异时滞系统鲁棒故障诊断滤波器设计问题. 把基于观测器的故障诊断滤波器作为残差产生器, 将故障诊断滤波器设计归结为H∞滤波问题, 使产生的残差信号即为故障的H∞估计, 给出了鲁棒H∞故障诊断滤波器存在的充分条件, 并利用锥面互补线性化迭代算法得到了故障诊断滤波器设计的线性矩阵不等式求解方法. 算例验证了算法的有效性.     

动态系统的鲁棒容错控制方法

本文针对有参数不确定性的动态系统设计了一种参数鲁棒故障检测器及故障向量辨识 器,提出了状态向量和输出向量的校正方法.最后给出应用所提算法对飞机实现鲁棒容错控 制的例子,数字仿真试验表明效果良好.     

鲁棒逆奈奎斯特方法中鲁棒Gershgori带的近似估计

针对多输入多输出线性系统的鲁棒逆奈奎斯特阵列分析,提出一种保守性较小的鲁棒Gershgorin带近似估计方法.首先给出一个保守性较小的鲁棒对角优势性引理,基于此引理,对具有参数不确定性的传递函数矩阵,推导了鲁棒Gershgorin带的近似估计方法,降低了估计结果的保守性.最后给出了仿真验证.     

基于H∞性能的不确定多速率系统鲁棒预测控制

研究含有不确定性的输入多采样率控制系统的鲁棒预测控制问题,提出了基于Hoo性能的鲁棒预测控制算法.该算法采用线性矩阵不等式(LMI)的方法,得出闭环多采样率系统具有H∞性能指标的上界γ,并给出保证闭环系统鲁棒稳定的判据.仿真结果表明了该算法的有效性.     

线性分式扰动下奇异系统鲁棒滤波递推算法

研究了线性分式扰动下线性奇异系统的状态估计问题, 给出了一种 Kalman 形式的递推滤波算法. 研究表明, 线性分式不确定性可以表示为一系列加性不确定性的交集. 本文讨论了如何寻找保守性最弱的加性不确定性来近似该交集, 并证明了该问题在鲁棒滤波过程中可以转化为凸优化问题. 数值仿真验证了上述算法的有效性. 对于具有结构约束的线性分式不确定性, 该算法的性能优于现有算法.     

集中式与分布式鲁棒状态融合估计

研究不确定多传感器系统的鲁棒估计问题是多传感器融合估计理论的一个重要研究方向.本文以鲁棒滤波理论为基础,给出了不确定多传感器系统的多胞型描述模型,并利用LMI方法给出集中式鲁棒状态融合估计问题的解,证明了将集中式鲁棒融合估计转化为相同估计性能的分布式融合估计算法的条件.最后给出了分布式不确定多传感器系统的状态融合估计的一个算例.     

不确定性离散时间系统非保守自适应鲁棒控制

研究含未知干扰和互质因子摄动离散时间不确定性系统的自适应鲁棒控制问题，为非保守的自适应鲁棒镇定提出一种广义参数递推估计方法，基于确定性等价原理，并利用ι1设计方法提出一种自适应鲁棒控制策略，证明了自适应算法的全局收敛性，给出了一个可验算的鲁棒稳定性条件，证明了该鲁棒稳定性条件是非保守和最优的。     

双容液位系统的不确定时滞的鲁棒容错控制

针对双容液位控制系统的执行器失效等故障,通过线性化建模,研究了鲁棒自适应容错控制问题。首先在系统无故障正常工作时,考虑建模误差、输入信号的稳定性、系统参数等不确定性因素,利用不确定性上界自适应估计,设计了不确定时滞鲁棒控制器。同时,对系统进行故障检测,研究了一种修正控制律的自适应鲁棒容错控制器设计方法,该控制器通过修补执行器故障所带来的影响使该系统最终有界稳定。最后,通过仿真试验,验证了提出的方法的有效性。     

双容液位系统的不确定时滞的鲁棒容错控制

针对双容液位控制系统的执行器失效等故障,通过线性化建模,研究了鲁棒自适应容错控制问题。首先在系统无故障正常工作时,考虑建模误差、输入信号的稳定性、系统参数等不确定性因素,利用不确定性上界自适应估计,设计了不确定时滞鲁棒控制器。同时,对系统进行故障检测,研究了一种修正控制律的自适应鲁棒容错控制器设计方法,该控制器通过修补执行器故障所带来的影响使该系统最终有界稳定。最后,通过仿真实验,验证了提出方法的有效性。     

Design and analysis of robust fault detection filter using LMI tools

In this paper, the robust fault detection filter design problem for linear time invariant (LTI) systems with unknown inputs and modeling uncertainties is studied. The basic idea of our study is to formulate the robust fault detection filter design as a H_∞ model-matching problem. A solution of the optimal problem is then presented via a linear matrix inequality (LMI) formulation. The main results include the formulation of robust fault detection filter design problems, the derivation of a sufficient condition for the existence of a robust fault detection filter and construction of a robust fault detection filter based on the iterative of LMI algorithm.     

Neural-network-based robust fault diagnosis in robotic systems

Fault diagnosis plays an important role in the operation of modern robotic systems. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical redundancy approach. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators with modeling uncertainties. A learning architecture with sigmoidal neural networks is used to monitor the robotic system for any off-nominal behavior due to faults. The robustness and stability properties of the fault diagnosis scheme are rigorously established. Simulation examples are presented to illustrate the ability of the neural-network-based robust fault diagnosis scheme to detect and accommodate faults in a two-link robotic manipulator.     

Robust state estimator design for uncertain linear systems using optimization techniques

The estimation of state variables of dynamic systems in noisy environments has been an active research field in recent decades. In this way, Kalman filtering approach may not be robust in the presence of modeling uncertainties. So, several methods have been proposed to design robust estimators for the systems with uncertain parameters. In this paper, an optimized filter is proposed for this problem considering an uncertain discrete-time linear system. After converting the subject to an optimization problem, three algorithms are used for optimizing the state estimator parameters: particle swarm optimization (PSO) algorithm, modified genetic algorithm (MGA) and learning automata (LA). Experimental results show that, in comparison with the standard Kalman filter and some related researches, using the proposed optimization methods results in robust performance in the presence of uncertainties. However, MGA-based estimation method shows better performance in the range of uncertain parameter than other optimization methods.     

Robust fault diagnosis for non-Gaussian stochastic systems based on the rational square-root approximation model

The task of robust fault detection and diagnosis of stochastic distribution control （SDC） systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Conver- gency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demon- strate the effectiveness of the proposed algorithm.     

Sensor bias fault diagnosis in a class of nonlinear systems

This note describes a robust sensor bias fault diagnosis architecture for dynamic systems represented by a class of nonlinear discrete-time models. The nonlinearity in the system nominal model is assumed to be a function of inputs and outputs only. Specifically, this note uses adaptive techniques to estimate an unknown sensor bias in the presence of modeling uncertainties. A simulation example is presented to illustrate the methodology. The robustness, sensitivity and stability properties of the bias fault diagnosis architecture are rigorously analyzed     

Robust adaptive fault accommodation for a robot system using a radial basis function neural network

A discrete-time radial basis function (RBF) neural network is designed for the fault accommodation of robotic systems. A robust learning algorithm using the adaptive dead-zone technique is presented to train the network parameters (weights and centres). This scheme assures the convergence of the estimate errors of both the neural network and the fault-monitoring system in the presence of system uncertainties. Simulations have been done on applying the RBF-network-based fault accommodation scheme to a two-link robotic manipulator. The main advantage of the adaptive algorithm is that the upper bound of system uncertainties is not known in advance, which makes the system more practical for the fault accommodation scheme as demonstrated.     

A Delay‐Dependent Approach to Robust Fast Adaptive Fault Estimation Design for Uncertain Neutral Systems with Time‐Varying Interval Delay

This paper studies the problem of robust fault estimation for neutral systems, which are subjected to uncertainties, actuator fault, time‐varying interval delay, and norm‐bounded external disturbance. Based on the fast adaptive fault estimation (FAFE) algorithm, we focus on the design of a fault estimation filter that guarantees stability in the filtering error system with a prescribed H_∞ performance. A novel Lyapunov‐Krasovskii functional is employed, which includes time delay information. A delay‐dependent criterion of robust fault estimation design is obtained by employing the free‐weighting matrices technique, and the proposed result has advantages over some existing results, in that it is less conservative and it enlarges the application scope. An improved sufficient condition for the existence of such a filter is proposed in terms of the linear matrix inequality (LMI) by the Schur complements and the cone complementary linearization algorithm. Finally, illustrative examples are provided to show the effectiveness of the proposed method.     

Actuator fault‐tolerant control (FTC) design with post‐fault transient improvement for application to aircraft control

A robust fault‐tolerant control scheme is proposed for uncertain nonlinear systems with zero dynamics, affected by actuator faults and lock‐in‐place and float failures. The proposed controller utilizes an adaptive second‐order sliding mode strategy integrated with the backstepping procedure, retaining the benefits of both the methodologies. A Lyapunov stability analysis has been conducted, which unfolds the advantages offered by the proposed methodology in the presence of inherent modeling errors and strong eventualities of faults and failures. Two modified adaptive laws have been formulated, to approximate the bounds of uncertainties due to modeling and to estimate the fault‐induced parametric uncertainties. The proposed scheme ensures robustness towards linearly parameterized mismatched uncertainties, in addition to parametric and nonparametric matched perturbations. The proposed controller has been shown to yield an improved post‐fault transient performance without any additional expense in the control energy spent. The proposed scheme is applied to the pitch control problem of a nonlinear longitudinal model of Boeing 747‐100/200 aircraft. Simulation results support theoretical propositions and confirm that the proposed controller produces superior post‐fault transient performance compared with already existing approaches designed for similar applications. Besides, the asymptotic stability of the overall controlled system is also established in the event of such faults and failures. Copyright © 2015 John Wiley & Sons, Ltd.     

Robust l1 estimation using the Popov-Tsypkin multiplier with application to robust fault detection

This paper considers the design of robust l₁ estimators based on multiplier theory (which is intimately related to mixed structured singular value theory) and the application of robust l₁ estimators to robust fault detection. The key to estimator-based, robust fault detection is to generate residuals which are robust against plant uncertainties and external disturbance inputs, which in turn requires the design of robust estimators. Specifically, the Popov-Tsypkin multiplier is used to develop an upper bound on an l₁ cost function over an uncertainty set. The robust l₁ estimation problem is formulated as a parameter optimization problem in which the upper bound is minimized subject to a Riccati equation constraint. A continuation algorithm that uses quasi-Newton BFGS (the algorithm of Broyden, Fletcher, Goldfab and Shanno) corrections is developed to solve the minimization problem. The estimation algorithm has two stages. The first stage solves a mixed-norm H₂/l₁ estimation problem. In particular, it is initialized with a steady-state Kalman filter and, by varying a design parameter from 0 to 1, the Kalman filter is deformed to an l₁ estimator. In the second stage the l₁ estimator is made robust. The robust l₁ estimation framework is then applied to the robust fault detection of dynamic systems. The results are applied to a simplified longitudinal flight control system. It is shown that the robust fault detection procedure based on the robust l₁ estimation methodology proposed in this paper can reduce false alarm rates.