含有未知输人及非强可检测子系统的线性切换系统的观测器设计

主要研究含有未知输入的线性切换系统的观测器设计问题.系统中包含有强可检测和非强可检测的子系统.首先,给出了状态及输出的坐标变换,通过坐标变换,系统的一部分子系统将不受未知输入的影响.其次,当未知输入的扰动矩阵线性相关,可以设计出不含状态跳跃的观测器.更一般的,如果扰动矩阵线性无关,可以设计含有状态跳跃的观测器.基于平均驻留时间的假定,可以得到重构状态的误差指数收敛于0.最后给出数值仿真验证理论结果.     

一种基于奇异摄动理论的鲁棒自适应非仿射非线性控制方法

文章针对一类具有参数不确定性和未知扰动项的非仿射非线性系统,提出了一种基于奇异摄动理论的鲁棒自适应控制方法.首先,通过控制输入构建了一个快变子系统,为原系统引入时标分离特性,使闭环系统可以在快变和慢变时间尺度上分解为两个降阶子系统:边界层子系统和降阶慢变子系统.在快时间尺度上,通过设计边界层子系统的结构使其在平衡点处指数稳定;在慢时间尺度上,针对含有参数不确定性和未知扰动项的降阶慢变子系统设计鲁棒自适应控制器.根据奇异摄动理论,闭环系统的跟踪性能可由降阶慢变子系统近似.文章提出的控制方法同时考虑了参数不确定性和未知扰动项的影响,在不忽略非仿射结构的前提下实现控制目标,不依赖于原系统的时标分离特性,且避免了反步法中的“复杂性爆炸”问题.两组与参考文献控制方法的对比仿真结果验证了文章控制方法的有效性.     

普适双变量随机气候模式的研究

该文对海气耦合双变量随机气候模式进行了系统研究，研究结果表明：（1）无论是考虑只有大气子系统的随机作用还是同时考虑大气子系统和海洋子系统的双扰动，对给定的扰动频率，海气气候系统对大气的扰动作用相当于一个十分简单的线性放大器；对于不同的扰动频率，在能量上海温的变化将随大气的扰动做4次幂的非线性响应;（2）若同时考虑大气子系统和海洋子系统的随机扰动，在满足细致平衡条件下可以求出系统定态的概率分布。否则，只有当扰动为弱噪声时，才能用级数展开法近似求出系统的概率分布。     

具有Markov跳变参数的广义系统鲁棒故障检测

针对一类广义不确定时滞Markov跳变系统,应用鲁棒控制理论分析重构的增广系统,获取了系统的故障检测滤波器.利用构造的随机Lyapunov-Krasovskii函数和线性矩阵不等式,证明并给出了故障检测滤波器有解的充分条件和优化设计方法,同时使得残差体现其对扰动的抑制作用和对故障的灵敏性.所设计的滤波器使系统具有随机稳定性,抑制干扰能力强,满足所给的范数指标.仿真算例对结果进行了验证,在故障信息发生时,故障检测滤波器可以很灵敏地检测出故障,并能有效地抑制未知扰动对残差的影响在给定的范围内.     

坦克炮控伺服系统的自适应鲁棒控制研究

针对一类坦克炮控伺服系统,充分考虑系统中的未知齿隙非线性及摩擦非线性,将这些未知非线性的影响表示成有界扰动项与未知非线性动态项之和,借助ARC(自适应鲁棒控制)的思想设计控制器,当未知非线性动态项为零时,控制器即为自适应控制器,保持系统稳定的同时实现对参考信号的精确跟踪,而当系统存在未知非线性动态项时,控制器具有很好的鲁棒性,保持系统所有信号有界的同时实现对参考信号的误差跟踪,且跟踪误差可以由设计参数的取值设定来任意调节,与现有结果相比,控制器的设计建立在充分考虑系统未知非线性的影响之上,从而避免了简单的把未知非线性影响简单的考虑成系统"总扰动"所造成的被控系统性能的损失。     

关联系统可靠镇定的开关分层控制法

本文建立了一种具有开关的双层控制方案,其中每一层均采用分散控制结构.并且只有第二层带有开关参数.进而说明,对一个由两个线性时不变子系统任意线性关联构成的大系统,存在所述的分层控制器使得导致的闭环系统,在任意关联结构扰动下都保持稳定的充分必要条件,是原正常关联的大系统作为具有双通道的分散系统,没有不稳定的固定模并且每个子系统均是能稳、能检测的.     

一类大型互联非线性系统的鲁棒分散控制

本文对带有界扰动的一类大型互联非线性系统进行了分散状态反馈控制设计,通过子系统状态的线性变换,得到分散状态反馈控制律.当状态反馈控制律作用于该系统时,无扰动的闭环系统是渐近稳定的.当扰动较小时,系统的状态能够收敛到原点的一个小邻域内.     

不确定临界混沌系统的有限时间同步与参数识别

针对不确定临界混沌系统,研究了它的有限时间同步和参数识别.基于有限时间李雅谱诺夫稳定性理论以及自适应控制技术,通过分步骤,设置合适的控制器,分别使得在参数具有扰动和参数完全未知的情况下,不确定的驱动系统和响应系统达到有限时间同步;同时,对参数未知的驱动系统,利用有限时间同步识别出未知参数.最后的数值模拟验证了所提出的方法的正确性和有效性.     

考虑时变状态约束与输入饱和的PMSM随机系统指令滤波控制

针对考虑时变状态约束和输入饱和的永磁同步电机随机系统的位置跟踪控制问题,提出了一种基于障碍Lyapunov函数的指令滤波反步控制方案.首先,构造障碍Lyapunov函数以保证电流、转速等状态量不违反时变约束条件.随后,利用模糊逻辑理论处理电机随机系统中的未知非线性项.此外,采用了指令滤波技术与误差补偿机制相结合的方法,不仅解决了传统反步法中出现的“计算爆炸”问题,而且消除了滤波误差的影响.仿真结果表明该控制器能有效抑制输入饱和与随机扰动的影响,提高系统的控制性能,同时能够保证电机系统所有状态在给定的约束范围内.     

有约束条件时混合线性模型的最优估计量公式

在数理统计中研究了线性模型的回归系数(未知参量)的估计问题,在滤波问题中研究的是利用量测值(随机向量)去估计系统的状态(随机向量)。然而在实际问题中,一些系统的量测值不仅依赖于系统的状态(随机向量),而且还依赖于其它的一些因素,其参数(回归系数)是未知的,因而其量测值含有未知参量,我们的目的是需要利用这种含有未知     

Worst-Case Fault Detection Observer Design: Optimization Approach

This paper deals with the fault detection problem for linear systems with unknown inputs. The H _∞ norm and H ₋ index are employed to measure the robustness to unknown inputs and the sensitivity to faults, respectively. By using the pole assignment approach, the fault detection problem is transformed to an unconstrained optimization problem. With the aid of a gradient-based optimization approach, an explicit formula for designing the desirable observer gain is derived. Furthermore, the fault sensitivity over a finite frequency range can also be solved by the proposed method. The methodology proposed is verified through numerical simulation studies performed on the fault detection observer design of a vertical takeoff and landing aircraft. This work was supported in part by DSO National Laboratories under Grant DSOCL-01144, by Nanyang Technological University under Grant RGM 34/01, and by HKU CRCG 10204304. Part of this work was presented in 2004 American Control Conference [1].     

组合群验在数控机床可靠性方面的应用

对数控机床子系统或子系统单元定量分析,得到了数控机床串联系统可靠度;利用组合群验数学模型,依据数控机床串联系统模型可靠度评价子系统的可靠性,也可为子系统故障分析提供方法.     

Dynamics analysis and numerical simulations of a stochastic non-autonomous predator–prey system with impulsive effects

In this paper, we propose a stochastic non-autonomous Lotka–Volterra predator–prey model with impulsive effects and investigate its stochastic dynamics. We first prove that the subsystem of the system has a unique periodic solution which is globally attractive. Furthermore, we obtain the threshold value in the mean which governs the stochastic persistence and the extinction of the prey–predator system. Our results show that the stochastic noises and impulsive perturbations have crucial effects on the persistence and extinction of each species. Finally, we use the different stochastic noises and impulsive effects parameters to provide a series of numerical simulations to illustrate the analytical results.     

Stabilization of active fault‐tolerant control systems by uncertain nonhomogeneous markovian jump models

This article investigates the stabilization and control problems for a general active fault‐tolerant control system (AFTCS) in a stochastic framework. The novelty of the research lies in utilizing uncertain nonhomogeneous Markovian structures to take account for the imperfect fault detection and diagnosis (FDD) algorithms of the AFTCS. The underlying AFTCS is supposed to be modeled by two random processes of Markov type; one characterizing the system fault process and the other describing the FDD process. It is assumed that the FDD algorithm is imperfect and provides inaccurate Markovian parameters for the FDD process. Specifically, it provides uncertain transition rates (TRs); the TRs that lie in an interval without any particular structures. This framework is more consistent with real‐world applications to accommodate different types of faults. It is more general than the previously developed AFTCSs because of eliminating the need for an accurate estimation of the fault process. To solve the stabilizability and the controller design problems of this AFTCS, the whole system is viewed as an uncertain nonhomogeneous Markovian jump linear system (NHMJLS) with time‐varying and uncertain specifications. Based on the multiple and stochastic Lyapunov function for the NHMJLS, first a sufficient condition is obtained to analyze the system stabilizability and then, the controller gains are synthesized. Unlike the previous fault‐tolerant controllers, the proposed robust controller only needs to access the FDD process, besides it is easily obtainable through the existing optimization techniques. It is successfully tested on a practical inverted pendulum controlled by a fault‐prone DC motor. © 2016 Wiley Periodicals, Inc. Complexity 21: 318–329, 2016     

Detecting and approximating fault lines from randomly scattered data

Discretely defined surfaces that exhibit vertical displacements across unknown fault lines can be difficult to approximate accurately unless a representation of the faults is known. Accurate representations of these faults enable the construction of constrained approximation models that can successfully overcome common problems such as over-smoothing. In this paper we review an existing method for detecting fault lines and present a new detection approach based on data triangulations and discrete Gaussian curvature (DGC). Furthermore, we show that if the fault line can be described non-parametrically, then accurate support vector machine (SVM) models can be constructed that are independent of the type of triangulation used in the detection algorithms. We shall also see that SVM models are particularly effective when the data produced by the detection algorithms are noisy. We compare the performances of the various new and established models.     

Likelihood-based confidence bands for fault lines in response surfaces

 We consider the problem of constructing asymptotic confidence bands, both pointwise and simultaneous, for a smooth fault line in a response surface when the design is represented by a point process, either deterministic or stochastic, with intensity n diverging to infinity. The estimator of the fault line is defined as the ridge line on the likelihood surface which arises from locally fitting a model that employs a linear approximation to the fault line, to a kernel smooth of the data. The construction is based on analysis of the limiting behaviour of perpendicular distance from a point on the true fault line to the nearest point on the ridge. We derive asymptotic properties of bias, and the limiting distribution of stochastic error. This distribution is given by the location of the maximum of a Gaussian process with quadratic drift. Although the majority of attention is focused on the regression problem, the limiting distribution is shown to have wider relevance to local-likelihood approaches to fault line estimation for density or intensity surfaces. Received: 20 August 2000 / Revised version: 27 September 2001 / Published online: 22 August 2002     

Performance evaluation of a multi-echelon production,transportation and distribution system: A matrix analytical approach

This study proposes an originative method to evaluate complex supply chains. A tentative multi-echelon production, transportation and distribution system with stochastic factors built-in is employed as a test bed for the proposed method. The supply subsystem formulated in this study is a two-stage production facility with constant probability of feedback and stochastic breakdowns. The transportation subsystem is a service facility with one server. The distribution subsystem under study is a single central warehouse with M retailers. All the participants of the supply chain use base-stock policies and single-server settings. We investigated both the make-to-order (MTO) and make-to-stock (MTS) policies for different base-stock levels, as adopted at different sites. Applying quasi-birth-and-death (QBD) processes as decomposed building blocks and then using the existing matrix analytical computing approach for the performance evaluation of a tandem queue constitutes the main procedure of this study. We also discuss the possibilities of extending the current model to account for other inventory control policies as well as for multi-server case. Numerical study shows our proposed analytical model is robust for practical use.     

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.     

Gain-scheduled fault detection on stochastic nonlinear systems with partially known transition jump rates

In this paper, the problem of continuous gain-scheduled fault detection (FD) is studied for a class of stochastic nonlinear systems which possesses partially known jump rates. Initially, by using gradient linearization approach, the nonlinear stochastic system is described by a series of linear jump models at some selected working points. Subsequently, observer-based residual generator is constructed for each jump linear system. Then, a new observer-design method is proposed for each re-constructed system to design H_∞ observers that minimize the influences of the disturbances, and to formulate a new performance index that increase the sensitivity to faults. Finally, continuous gain-scheduled approach is employed to design continuous FD observers on the whole nonlinear stochastic system. Simulation example is given to show the effectiveness and potential of the developed techniques.