基于动态神经网络的非线性系统鲁棒故障检测

针对一类具有不确定性扰动的非线性系统,将设计的系统线性观测器产生的误差信号作为残差,采用一种具有高斯型激励函数的动态神经网络(DNN)对残差信号进行分析处理,得到了系统的鲁棒故障检测方法.文中分析了该方法的稳定性和故障检测的鲁棒性,并通过算例验证了该方法的有效性.     

具有未知扰动的广义系统的故障检测

研究具有未知扰动的随机广义系统的故障检测问题,在合理的条件下,利用状态变换从原系统中分离出一个与未知扰动解耦的降阶子系统,构造了该随机子系统稳定的滤波器,给出了输出偏差量的统计特性,在此基础上,实现了系统故障的检测．     

Delta算子描述不确定离散故障检测系统H_∞滤波器设计

考虑故障检测对象参数发生加性范数有界摄动的情况,基于线性矩阵不等式(LMI)方法,对一类由Delta算子描述的不确定离散故障检测系统的鲁棒H_∞故障检测滤波器设计问题进行研究.通过构建增广系统,利用Lyapunov函数理论和Schur补定理,把H_∞故障检测滤波器存在的充分条件转化为LMI形式.通过求解数值算例并作对比分析,表明考虑对象参数发生摄动所设计的鲁棒H_∞故障检测滤波器能正确有效地检测到故障信号,而不考虑对象参数发生摄动所设计的H_∞故障检测滤波器将不能正确有效地检测到故障信号.     

具时变时滞和 Markovian转换的不确定奇异随机系统的鲁棒 H_∞ 滤波(英文)

本文处理了一类具与模式有关的时变时滞和 Markovian转换的不确定奇异随机系统的鲁棒H∞滤波问题.所考虑的系统包含参数不确定性,Markovian参数,随机扰动和与模式有关的时变时滞.本文的目的是设计一个滤波器以保证滤波错误系统是正则的、无脉冲的、鲁棒指数均方稳定的和可达到一个给定的 H∞扰动衰减水平.文章首先得到所求鲁棒指数H∞滤波器存在的充分条件,然后给出所求滤波器参数的显示表示.     

具有信道衰落的离散系统非脆弱H_∞故障检测

文章设计了一种具有信道衰落的离散系统的非脆弱H_∞故障检测滤波器.用L阶莱斯衰落模型刻画实际接收到的测量信号,其信道系数具有在区间[0,1]上的任一概率密度函数.针对滤波器实际执行时增益发生变化的现象,采用乘性增益变化进行描述.基于李雅普诺夫稳定性理论、随机分析技术及LMI技术,得到使整个滤波误差系统随机稳定、同时满足H_∞性能指标的非脆弱故障检测滤波器存在的充分条件,给出离散系统的非脆弱H_∞故障检测问题的求解方案.该方案既能使滤波误差系统稳定,又能保证系统具有一定的H_∞性能.最后,由数值仿真说明研究的有效性.     

基于ADRC的小型四旋翼姿态控制实现

设计了一个四旋翼飞行系统,提出了一种自抗扰控制方法,解决了该飞行系统的姿态控制问题.将显性互补滤波器与扩张状态观测器相结合,实现了对系统的状态与内外总扰动的实时估计,并在此基础上利用非线性误差反馈控制律对该扰动进行了补偿,消除了内外扰动对系统的影响.最后,分别采用PID与ADRC算法实现四旋翼的姿态控制,对比结果验证了所采用的自抗扰控制方法的有效性和优越性.     

基于随机Hamilton实现的车摆系统干扰抑制控制

研究车摆系统在外部随机激励以及不确定扰动同时作用下的干扰抑制控制问题.首先给出了系统的随机不确定非线性动态模型,并通过反馈控制完成了系统的Hamilton实现,分析了系统的耗散性能.然后基于系统的耗散实现设计了干扰抑制控制器,该控制器设计能够充分利用系统的能量产生、耗散和转换特性.仿真结果验证了文章方法的有效性.     

基于动态面技术的随机非线性关联系统的输出反馈控制

针对一类参数不确定且含有未知关联项的随机非线性关联系统,提出了一种自适应输出反馈控制方案.该方案利用K滤波器估计不可量测的系统状态,将动态面技术与输出反馈控制相结合,避免了传统后推设计过程中对虚拟控制律反复求导出现的"复杂性膨胀"问题,设计的自适应动态面控制器可以确保闭环系统中的所有信号在概率意义下半全局一致终结有界,且跟踪误差收敛到一个小的残差集内.     

基于干扰观测器的异构多智能体自适应滑模容错一致性控制

针对线性一阶和二阶异构多智能体系统,考虑到任意智能体可能发生的执行器故障以及受到外部干扰,研究了系统容错一致性控制设计问题.首先设计变增益扰动观测器,快速估计外部干扰;其次,利用一致性误差变量构造自适应积分滑模面,结合干扰观测器的估计值设计自适应滑模容错控制器.当异构多智能体系统存在执行器故障和外部扰动时,自适应滑模控制器可以保证智能体系统的位置和速度状态趋于一致.最后,利用Matlab仿真验证了所提方法的可行性与有效性.     

一类受随机扰动的动态优化问题的环境检测与响应

动态优化问题在实际生产或生活中广泛存在,其中环境检测与响应方法是解决此类问题的核心.在许多实际问题中,由于随机因素的干扰,优化问题的真实最优解会在一定程度上发生随机偏移,该文考虑最优解随机偏移服从正态分布的随机动态优化问题.首先,该文改进了现有基于正交试验设计思想的区间收缩方法,进而提出了动态优化问题的环境检测与响应策略,在一定程度上避免了已有方法的盲目性与随机性.其次,给出了扰动前后环境检测无变化所对应随机扰动的标准差上限.最后利用粒子群算法进行测试,实验结果表明:该文提出的环境检测与响应方法不仅能够有效处理最优解受随机扰动的随机动态优化问题,而且也能提高利用粒子群算法处理其它动态优化问题的能力.改进的环境检测与响应方法可以应用到粒子群算法外的其它演化算法上.     

Observer-based fault detection for networked discrete-time infinite-distributed delay systems with packet dropouts

This paper addresses the problem of fault detection for networked discrete-time infinite-distributed delay systems with packet dropouts. Both sensor-to-controller and controller-to-actuator packet dropouts are described by two different Bernoulli distributed white sequences, respectively. The problem addressed is to design an observer-based fault detection filter (FDF) such that the error between the residual and the fault is made as small as possible. Unlike most of the existing literature, we have noted that the control input of the observer is different from that of the plant because of packet dropouts in the controller-to-actuator link. Sufficient condition for the existence of the FDF is derived in terms of some linear matrix inequalities (LMIs). When these LMIs are feasible, the explicit expression of the desired FDF can also be characterized. A numerical example is exploited to show the effectiveness of the obtained results.     

Stability and robustness properties of a class of discrete–time residual generators

The aim of this paper is to present a design methodology for an observer–based fault detection filter. This approach allows to compute a filter gain such that both fault detection and disturbance attenuation requirements are accomplished. The developments are based on H_∞ optimization in the discrete–time framework. The existence conditions for the observer–based detection filter are expressed in terms of feasibility of a system of matrix inequalities. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Robust fault detection of Markovian jump systems with different system modes

This paper deals with the robust fault detection filter (RFDF) design problems for uncertain nonlinear Markovian jump systems with unknown input. By using a observer-based fault detection filter as residual generator, the RFDF design is formulated as an _H∞$H_{\infty }$-filtering problem. Particularly, two different Markov processes are considered for modeling the randomness of system matrix and the state delay. With the aid of the weighting matrix function, the design objective is to find an optimal RFDF, which results in a minimal difference between the reference model and the RFDF to be designed. By using a new convex polyhedron technique and two mode-dependent Lyapunov functional, some new sufficient conditions are established in terms of delay-dependent linear matrix inequalities (LMIs) to synthesize the residual generation scheme. Finally, a numerical example is given to illustrate the effectiveness of the proposed techniques.     

Adjustable dimension descriptor observer based fault estimation for switched nonlinear systems with partially unknown nonlinear dynamics

This paper focuses on the fault estimation problem for switched systems with partially unknown nonlinear dynamics, actuator and sensor faults, simultaneously. The fault estimation observers are constructed, in which the observer dimension is not fixed and can be selected in a certain range. Both the disturbance decoupling and disturbance attenuation are considered, where the unknown nonlinear dynamics can be decoupled and the effect of modeling error and measurement disturbance is attenuated. Based on the average dwell time and the piecewise Lyapunov function, the observer parameter matrices can be calculated by solving LMIs and matrix equations. Finally, two examples are listed to verify the proposed fault estimation approach.     

Filtering-based robust fault detection of fuzzy jump systems

This paper studies the robust fault detection filter (RFDF) design problems for uncertain nonlinear Markov jump systems with state delays and parameter uncertainties. By means of Takagi-Sugeno fuzzy models, the dynamics of filtering error generator and the fuzzy RFDF system are constructed. With the aid of the selected weighting matrix function, the design objective is to find an optimal RFDF which results in a minimal difference between the reference model (ideal solution) and the RFDF (real solution) to be designed. A sufficient condition is firstly established on the stochastic stability by using stochastic Lyapunov-Krasovskii functional approach. Then in terms of linear matrix inequalities techniques, sufficient conditions on the existence of fuzzy RFDF are presented and proved. Finally, the design problem is formulated as an optimization algorithm. Simulation results illustrate that the proposed RFDF can detect the faults shortly after the occurrences.     

Mode Dependent H∞ Filtering for Discrete-Time Networked Systems with Random Measurement Missing and Delays

The article investigates the H_∞ filtering problem for a class of discrete-time networked systems with random measurement losses and delays. Markov chain is used here to model measurement losses and delays in a unified framework. Based on the mode-dependent Lyapunov function approach, the necessary and sufficient conditions are derived to guarantee the exponential stability with a prescribed H_∞ disturbance attenuation performance for the filtering error system. By using a novel design scheme, the explicit expressions of mode dependent filter parameters are given in the form of linear matrix inequalities (LMIs) which can be readily solved by using the LMI TOOLBOX in MATLAB. At last, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.     

Robust state estimation and fault diagnosis for uncertain hybrid systems

Robust state estimation and fault diagnosis are challenging problems in the research into hybrid systems. In this paper a novel robust hybrid observer is proposed for a class of hybrid systems with unknown inputs and faults. Model uncertainties, disturbances and faults are represented as structured unknown inputs. The robust hybrid observer consists of a mode observer for mode identification and a continuous observer for continuous state estimation and mode transition detection. It is shown that the mode can be identified correctly and the continuous state estimation error is exponentially uniformly bounded. Robustness to model uncertainties and disturbances can be guaranteed for the hybrid observer by disturbance decoupling. Furthermore, the detectability and mode identifiability conditions are rigorously analyzed. On the basis of the robust hybrid observer, a robust fault detection and isolation scheme is presented also in the paper. Simulations of a hybrid four-tank system show the proposed approach is effective.     

L2 − L∞ filtering for Markovian jump systems with time-varying delays and partly unknown transition probabilities

This paper considers the L₂ − L_∞ filtering problem for Markovian jump systems. The systems under consideration involve time-varying delays, disturbance signal and partly unknown transition probabilities. The aim of this paper is to design a filter, which is suitable for exactly known and partly unknown transition probabilities, such that the filtering error system is stochastically stable and a prescribed L₂ − L_∞ disturbance attenuation level is guaranteed. By using the Lyapunov-Krasovskii functional, sufficient conditions are formulated in terms of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed main results. All these results are expected to be of use in the study of filter design for Markovian jump systems with partly unknown transition probabilities.