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.     

可逆冷带轧机速度张力多变量耦合系统的建模及分散控制

针对具有多变量、非线性、强耦合和不确定性的可逆冷带轧机速度张力系统,提出了一种基于扩张状态观测器(extended state observer,ESO)的全局积分滑模自适应反步分散控制方法.首先,采用机理建模方法,建立了相对完备的可逆冷带轧机速度张力多变量耦合系统的数学模型.其次,将各子系统的耦合项和不确定项看成外扰,通过构造的ESO对其进行动态观测,并分别引入所设计的全局积分滑模自适应反步控制器中进行补偿,速度张力系统实现了有效的动态解耦和协调控制.理论分析表明,所提出的控制方法能够保证滑模面的渐近稳定和闭环系统的渐近跟踪性能.最后,基于某1422mm可逆冷带轧机速度张力系统的实际数据进行仿真,结果验证了所提方法的有效性.     

非仿射非线性离散系统的数据驱动二阶滑模解耦控制

针对传统二阶离散滑模变结构控制方法很难应用于模型未知系统的问题,提出了一种数据驱动二阶滑模解耦控制算法.该控制算法采用数据驱动的策略,运用系统的I/O数据,实时计算二阶离散滑模控制(2–DSMC)律.同时,运用观测器的思想,在控制器设计中引入离散扩张状态观测器(DESO),在线估计系统各回路间耦合、不确定性和外部扰动,进一步实现系统的解耦,改善控制品质.最后的理论分析和仿真结果表明,所提出的方法对于一般带有扰动和不确定性的非仿射非线性离散多入多出(MIMO)系统具有较好的解耦效果、渐进收敛的稳定性和较强的鲁棒性.     

基于重叠片排列的流形学习算法

针对线性数据降维算法对处理非线性结构数据的降维效果不是很好,提出一种基于重叠片排列的流形学习算法,该算法根据局部的线性贴片处在非线性流形中的特性,将流形划分为线性互相重叠的局部区域贴片,且利用主成分分析方法得到局部区域贴片的低维表示,然后排列且对齐其低维坐标,以获得整体数据的低维坐标.通过仿真结果证明,基于重叠片排列的流形学习算法在应用于人脸识别和分类问题时以及在识别准确率方面要优于其他经典的流形学习算法.     

Stochastically resilient extended Kalman filtering for discrete-time nonlinear systems with sensor failures

Missing sensor data is a common problem, which severely influences the overall performance of modern data-intensive control and computing applications. In order to address this important issue, a novel resilient extended Kalman filter is proposed for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations. The failure mechanisms of multiple sensors are assumed to be independent of each other with different failure rates. The locally unbiased robust minimum mean square filter is designed for state estimation under these conditions. The performance of the proposed estimation method is verified by means of numerical Monte Carlo simulation of two different nonlinear stochastic systems, involving a sinusoidal system and a Lorenz oscillator system.     

Modelling the dynamics of the students’ academic performance in the German region of the North Rhine-Westphalia: an epidemiological approach with uncertainty

Student academic underachievement is a concern of paramount importance in Europe, where around 15% of the students in the last high school courses do not achieve the minimum knowledge academic requirement. In this paper, we propose a model based on a system of differential equations to study the dynamics of the students’ academic performance in the German region of the North Rhine-Westphalia. This approach is supported by the idea that both good and bad study habits, are a mixture of personal decisions and influence of classmates. This model allows us to forecast the student academic performance by means of confidence intervals over the next few years.     

Finite horizon optimal control of non-linear discrete-time switched systems using adaptive dynamic programming with ε-error bound

In this paper, we aim to solve the finite-horizon optimal control problem for a class of non-linear discrete-time switched systems using adaptive dynamic programming(ADP) algorithm. A new ε-optimal control scheme based on the iterative ADP algorithm is presented which makes the value function converge iteratively to the greatest lower bound of all value function indices within an error according to ε within finite time. Two neural networks are used as parametric structures to implement the iterative ADP algorithm with ε-error bound, which aim at approximating the value function and the control policy, respectively. And then, the optimal control policy is obtained. Finally, a simulation example is included to illustrate the applicability of the proposed method.     

一类非线性随机时滞系统的故障诊断

针对一类非线性随机时滞系统提出了一种新的故障检测算法,该方法不同于传统的故障检测方法,是通过构建一种带有Consensus滤波器的故障诊断滤波器的方法来进行故障诊断.首先采用一组传感器测量系统实际输出,然后根据传感器的测量值构建一组残差生成器,将每个残差生成器看作一个小世界网络模型中的一个节点,采用动态Consensus算法计算出残差生成器的残差,并根据残差来判断系统是否有故障发生.仿真结果表明了本文所提出方法的可行性和有效性.     

Circle-criterion Based Nonlinear Observer Design for Sensorless Induction Motor Control

This paper deals with the design of a nonlinear observer for sensorless induction motor control. Based upon the circle criterion approach, a nonlinear observer is designed to estimate pertinent but unmeasurable state variables of the considered induction machine for sensorless control purpose. The observer gain matrices are computed as a solution of linear matrix inequalities(LMI) that ensure the stability conditions of the state observer error dynamics in the sense of Lyapunov concepts. Measured and estimated state variables can be exploited to perform a state feedback control of the machine system. The simulation results are presented to illustrate the effectiveness of the proposed approach for nonlinear observer design.     

Robust Unknown Input Observer Design for Linear Uncertain Time Delay Systems with Application to Fault Detection

In this paper, a novel approach is proposed to design a robust fault detection observer for uncertain linear time delay systems. The system is composed of both norm‐bounded uncertainties and exogenous signals (noise, disturbance, and fault) which are considered to be unknown. The main contribution of this paper is to present unknown input observer (UIO)‐based fault detection system which shows the maximum sensitivity to fault signals and the minimum sensitivity to other signals. Since the system contains uncertainty terms, an H_∞ model‐matching approach is used in design procedure. The reference residual signal generator system is designed so that the fault signal has maximum sensitivity while the exogenous signals have minimum sensitivity on the residual signal. Then, the fault detection system is designed by minimizing the estimation error between the reference residual signal and the UIO residual signal in the sense of H_∞ norm. A sufficient condition for the existence of such a filter is exploited in terms of certain linear matrix inequalities (LMIs). Application of the proposed method in a numerical example and an engineering process are simulated to demonstrate the effectiveness of the proposed algorithm. Simulation results show the validity of the proposed approach to detect the occurrence of faults in the presence of modeling errors, disturbances, and noise.