基于未知输入观测器的导弹间歇故障诊断

针对带有未知扰动和噪声的导弹间歇故障诊断问题,设计了一种基于未知输入观测器的导弹问题故障诊断方法,系统的输入部分或全部未知情况下也能获取系统状态的称为未知输入观测器.首先,为实现对外部扰动的解耦,设计降维未知输入观测器,并通过滑动时间窗口得到对间歇故障敏感而对未知扰动解耦的残差信号;然后,在满足误报率和漏报率的条件下,通过假设检验,确定了间歇故障发生时刻和消失时刻的可检测阈值;最后,对所提出的方法进行了仿真验证.仿真结果表明,在误差允许的范围内,设计的方法能够实现对间歇故障检测,满足实时性和准确性的要求.     

AGC系统中的非线性观测器设计

AGC（自动发电控制）系统是一个十分复杂的系统，其具有严重的非线性，且输入（锅炉、轮机等动力产生部分）和输出（频率负载）均存在未知扰动，为了实现对发电系统的精确控制，设计出输入输出扰动的观测器显得尤为重要。在阐述了一种带有未知输入输出扰动的非线性观测器设计方法的同时，是对传统的只能观测输入环节中的未知扰动观测器的一大改进。通过对系统状态方程和输出方程的一系列解偶变换，将输入、输出扰动从系统中分离出来。解偶后，系统分为受扰子系统和无扰动子系统两部分，通过对无扰动子系统的分析，最终设计出输入输出未知扰动观测器。系统观测器增益矩阵则通过求解一个代数Riccati方程（ARE）得到。将设计出的观测器用于故障检测与隔离（FDI）并取得良好的效果。     

AGC系统中的非线性观测器设计

AGC(自动发电控制)系统是一个十分复杂的系统,其具有严重的非线性,且输入(锅炉、轮机等动力产生部分)和输出(频率负载)均存在未知扰动,为了实现对发电系统的精确控制,设计出输入输出扰动的观测器显得尤为重要.在阐述了一种带有未知输入输出扰动的非线性观测器设计方法的同时,是对传统的只能观测输入环节中的未知扰动观测器的一大改进.通过对系统状态方程和输出方程的一系列解偶变换,将输入、输出扰动从系统中分离出来.解偶后,系统分为受扰子系统和无扰动子系统两部分,通过对无扰动子系统的分析,最终设计出输入输出未知扰动观测器.系统观测器增益矩阵则通过求解一个代数Riccati方程(ARE)得到.将设计出的观测器用于故障检测与隔离(FDI)并取得良好的效果.     

基于未知输入观测器的非线性系统故障诊断

针对一类含未知输入和执行器故障的非线性系统,提出基于未知输入观测器的故障诊断算法,改进了Luenberger故障诊断观测器对系统出现未知扰动时的不足.利用广义逆方法,将未知输入从残差信号中完全解耦,通过产生对故障高敏感性以及对未知扰动强抗扰动性的观测器实现系统的故障诊断,并通过Lyapunov函数用线性矩阵不等式保证了系统稳定性.     

单区域负荷频率控制系统故障诊断研究

针对一类包含有干扰的负荷频率控制系统,提出一种利用未知输入观测器(UIO,unknown inputobserver)对单区域负荷频率控制系统执行机构进行故障诊断的方法.首先通过构建单区域负荷频率控制系统模型,给出系统的动态方程;其次,通过对系统中的加性未知干扰项进行解耦,构造一个使残差对未知输入具有鲁棒性,而对故障敏感的全阶未知输入观测器,以达到对执行器故障诊断的目的.最后通过Matlab仿真验证了所设计方法的正确性和可行性.     

具有未知扰动上界的气垫船全鲁棒滑模控制

本文以轨迹跟踪为任务目标,针对具有未建模动态,参数不确定和未知扰动的全垫升气垫船非线性动力学模型,提出了一种新颖的全鲁棒滑模变结构控制器,并且结合一种不需要扰动上界信息的综合观测器对系统的总扰动进行前馈补偿.首先给出气垫船四自由度的运动学和动力学矢量模型,在扰动上界未知的条件下,针对系统内外不确定性的本质不同,分别设计前馈补偿,同时加入补偿项进一步减小估计误差.然后,设计了一种新颖的无趋近过程的全鲁棒滑模控制器,保证高速行驶的气垫船在未知扰动的环境中仍能够较好的完成跟踪任务,减小跟踪误差的同时消除抖振,增强气垫船控制系统的鲁棒性.通过Young’s不等式和李雅普诺夫稳定性证明整个联动系统的稳定性,仿真结果表明该方法的有效性.     

基于有限时间未知输入观测器的一类受扰非线性系统故障检测与估计

针对一类非线性系统同时存在执行器故障、传感器故障和扰动的问题,提出一种基于有限时间未知输入观测器的故障检测与估计方法.首先,通过线性非奇异变换将原系统解耦为两个降阶的子系统,其中一个子系统只包含扰动,另一个子系统同时包含扰动和故障;然后,通过一阶低通滤波器获得新的状态并与子系统构成增广系统,实现将原系统的传感器故障转化为增广系统的执行器故障;接着,设计未知输入观测器对增广系统故障进行检测,实现在有限时间内估计出系统的扰动和故障,并通过理论分析验证所设计观测器的有限时间收敛性;最后,基于永磁同步电机(PMSM)转速系统进行仿真研究,仿真结果验证了所提出方法的有效性.     

基于未知输入集员滤波器的不确定系统故障诊断

针对一类具有参数不确定性和未知扰动的线性系统,提出了一种新的执行器故障诊断方法.将指定执行器故障视为未知输入,利用全对称多胞形近似状态边界,本文设计了一种未知输入全对称多胞形集员滤波器,以估计测量输出的上下边界.在此基础上,提出了一种利用一组未知输入滤波器的故障检测与分离策略.通过一个飞行控制系统的数值仿真验证了所提出方法的有效性.     

含未知扰动情况下的定性控制

针对模型中含有不确定因素的含未知扰动的非线性系统给出了一种基于定量模型的定性控制方法 ,此方法具有模糊控制的控制效果 ,避免了模糊控制器设计中存在主观性和模糊规则难以获取等缺点。仿真表明了该方法的有效性     

切换拓扑下非线性多智能体系统自适应神经网络一致性

本文研究一类具有未知控制系数的非线性多智能体系统自适应神经网络分布式控制策略.首先,针对切换拓扑下具有未知控制系数的非线性多智能体系统一致性问题,提出一类自适应神经网络一致性控制算法.其中,采用神经网络函数逼近方法解决系统中的不确定性问题,并设计一项自适应光滑项处理有界扰动和神经网络函数逼近误差.随后,证明了切换拓扑下具有未知控制系数的非线性多智能体系统的一致性,并保证了闭环系统的有界性.此外,本文把相关的一致性算法扩展到了一般有向图含有一个有向生成树的情形.最后,通过仿真实例验证了本文所提算法的有效性.     

未知输入和可测噪声重构之线性矩阵不等式非线性系统观测器设计

针对一类同时具有未知输入和输出可测噪声的Lipschitz非线性系统,讨论了状态估计、未知输入与可测噪声重构的问题.首先,基于广义系统和线性矩阵不等式的方法设计滑模未知输入观测器,不仅对原系统状态进行渐近估计,而且实现了对系统输出可测噪声的重构;其次,考虑一种鲁棒滑模微分器,实现了广义系统输出向量微分的精确估计,并在此基础上,提出了一种未知信息重构方法,该方法具有避免直接使用系统输出微分信息的优点.最后,对火车牵引拖动系统模型仿真,结果表明该方法不但能够实现对系统状态的估计,而且可以有效重构未知信息.     

一种改进的未知动态环境下机器人混合路径规划方法

动态未知环境下的机器人路径规划是机器人导航领域的重要课题之一,采用传统的方法求解并不理想。针对这个问题,提出一种改进的机器人混合路径规划方法。首先利用改进的文化基因算法规划出较优的全局路径,指引机器人沿着全局路径行走,然后根据传感器探测到的局部环境信息,利用Morphin算法进行局部路径实时规划,使机器人有效地躲避动态障碍物。仿真实验表明,该算法在未知动态路径规划中具有良好的效果。     

带不确定量测和未知虚警概率的粒子滤波器

为了解决带不确定量测和未知虚警概率的非线性非高斯系统状态估计问题,本文提出了一种新的粒子滤波方法,利用随机不确定量测模型来更新粒子和权值,并基于极大似然准则来辨识未知的虚警概率.本文所提出的带不确定量测和已知虚警概率的粒子滤波方法与现有标准的粒子滤波方法具有几乎一致的计算复杂度,但是更适合用于处理带不确定量测的非线性非高斯系统状态估计问题.此外,在状态转移密度函数被选择为建议密度函数时,本文证明了基于所提出的虚警概率辨识方法的极大似然估计唯一,从而为精确辨识虚警概率提供了理论保证.单变量非平稳增长模型和纯方位跟踪的数值仿真验证了所提出粒子滤波方法的有效性和与现有方法相比的优越性.     

前件变量未知的T-S模糊系统输出反馈控制

针对一类带有未知前件变量的T-S模糊系统,提出一种新的观测器-控制器设计方法.首先,在前件变量完全未知的情况下,将观测误差方程中由未知前件变量导致的相关项利用Lipschitz条件进行限制;然后,根据Lyapunov函数得到系统稳定性条件,同时提出一种基于特征值的矩阵缩放方法和一种基于模糊 Lyapunov 函数的高增益观测器方法计算观测器-控制器的增益矩阵;最后,针对动力定位船舶的控制问题对两种方法进行仿真验证和比较分析,以表明所提出方法的有效性.     

On stability analysis via Lyapunov exponents calculated based on radial basis function networks

The concept of Lyapunov exponents is a powerful tool for analysing the stability of nonlinear dynamic systems, especially when the mathematical models of the systems are available. For real world systems, such models are often unknown. Estimating Lyapunov exponents using a time series has the advantage in that no mathematical model is required. However the time-series-based methods are believed to be reliable only for estimating positive exponents. Furthermore, when nonlinear mapping is applied for deriving the neighbourhood-to-neighbourhood matrices, the loads of mathematical deduction and programming increase significantly, which makes it unfeasible to nonlinear systems with high dimensions. In contrary, the model-based methods are constructive and reliable for calculating both positive and non-positive exponents. The use of the system Jacobians is the key to the advantage of the model-based methods. In this article, a novel approach is proposed, where the system Jacobians are derived based on system approximation using the radial basis function network. The proposed method inherits the advantage of the model-based methods, yet no mathematical model is required. Two case studies are presented to demonstrate the efficacy of the proposed method. We believe that the work can contribute to the stability analysis of nonlinear systems of which the dynamics are either difficult to model due to complexities or unknown.     

Accelerating feature-vector matching using multiple-tree and sub-vector methods

We propose two methods to accelerate the matching of an unknown object with known objects, all of which are expressed as feature vectors. The acceleration becomes necessary when the population of known objects is large and a great deal of time would be required to match all of them. Our proposed methods are multiple decision trees and sub-vector matching, both of which use a learning procedure to estimate the optimal values of certain parameters. Online matching with a combination of the two methods is then performed, whereby candidates are matched rapidly without sacrificing the test accuracy. The process is demonstrated by experiments in which we apply the proposed methods to handwriting recognition and language identification. The speed-up factor of our approach is dramatic compared with an alternative approach that eliminates candidates in a deterministic fashion.     

Integral-equation approach to parameter identification without consideration of initial conditions

The widely used integral-equation approach for identification of continuous systems is investigated. Attention is focused on the initial condition problem which has been puzzling many researchers. A new calculation procedure for the multiple integrations of the system signal derivatives is proposed and a new integral-equation approach is proposed for which the initial conditions need not be identified as unknown parameters. Therefore, the burden of the identification algorithms can be greatly reduced compared to conventional methods. Discussions on the unification of the integral-equation approach with the other methods are also provided.     

Adaptive control of uncertain time-delay chaotic systems

This work investigates adaptive control of a large class of uncertain time_delay chaotic systems (UTCSs) with unknown general perturbation terms bounded by a polynomial (unknown gains). Associated with the different cases of known and unknown system matrices, two corresponding adaptive controllers are proposed to stabilize unstable fixed points of the systems by means of Lyapunov stability theory and linear matrix inequalities (LMI) which can be solved easily by convex optimization algorithms. Two examples are used for examining the effectiveness of the proposed methods.     

Adaptive control of uncertain time-delay chaotic

This work investigates adaptive control of a large class of uncertain time-delay chaotic systems （UTCSs） with unknown general perturbation terms bounded by a polynomial （unknown gains）, Associated with the different cases of known and unknowl system matrices, two corresponding adaptive controllers are proposed to stabilize unstable fixed points of the systems by means of Lyapunov stability theory and linear matrix inequafities （LMI） which can be solved easily by convex optimization algorithms, Two examples are used for examining the effectiveness of the proposed methods.     

Adaptive control of uncertain time-delay chaotic systems

This work investigates adaptive control of a large class of uncertain time_delay chaotic systems (UTCSs) with unknown general perturbation terms bounded by a polynomial (unknown gains). Associated with the different cases of known and unknown system matrices, two corresponding adaptive controllers are proposed to stabilize unstable fixed points of the systems by means of Lyapunov stability theory and linear matrix inequalities (LMI) which can be solved easily by convex optimization algorithms. Two examples are used for examining the effectiveness of the proposed methods.