非线性增益递归滑模动态面自适应NN控制

针对一类严反馈非线性不确定系统的跟踪控制问题,提出一种非线性增益递归滑模动态面 (Dynamic surface control, DSC)自适应控制方法. 通过设计一个新的非线性增益函数,并构造递归滑模动态面的控制策略和新的Lyapunov函数,同时利用神经网络在线逼近系统不确定项, 该方法有效解决了具有输入饱和约束条件下系统控制精度与动态品质间的矛盾,增强了控制器对其自身参数摄动的非脆弱性. 理论证明了闭环系统所有状态是半全局一致最终有界的,且跟踪误差可收敛至任意小.     

基于观测器的受正弦扰动MIMO系统最优跟踪控制

研究具有外界正弦扰动作用下MIMO系统的最优跟踪控制问题.根据扰动特点,假设拉格朗日算子化简求解系统两点边值问题,通过求解Riccati方程和矩阵方程分别在有限时域和无限时域得到系统前馈反馈最优跟踪控制律.证明了最优跟踪控制律的存在唯一性条件,并给出了最优跟踪控制律的实现算法.构造降维参考输入观测器解决了控制律中前馈项物理不可实现问题.仿真结果表明了该方法的有效性.     

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.     

基于不完全转移率的连续Markov 跳变奇异系统的H∞ 控制

研究一类不完全转移率信息的Markov跳变奇异系统的H∞控制问题,提出连续Markov跳变奇异系统的新型有界实引理,并将其推广到不完全转移率条件.进一步设计H∞状态反馈控制器,使得闭环系统在转移率部分未知的条件下随机可容许,且满足H∞性能H.所得结论涵盖了奇异矩阵模态依赖情形,且表示为严格线性矩阵不等式形式,利于工程实现.最后,通过仿真算例表明了所提出方法的有效性和优越性.     

基于四元数和增广矩阵束的MIMO雷达角度估计算法

通过研究多输入多输出（Multiple input and multiple out, MIMO）雷达的角度估计算法,基于收发共址的十字阵MIMO雷达系统,将四元数理论应用到 MIMO雷达角度中,提出了一种新的参数估计算法。通过构造四元数模型,结合增广矩阵束（ Matrix enhancement and matrix pencil, MEMP）方法构造增广矩阵,并证明该矩阵的秩 等于目标总数,且不受目标相干性的影响,结合ESPRIT算法实现了对MIMO相干目标的角度估 计。算法无需谱峰搜索,能够实现参数的自动配对,同时降低了运算复杂度。仿真实验进一 步验证了算法的有效性。     

工业PTA氧化过程4-CBA浓度的模糊神经网络模型

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.     

New results on PWARX model identification based on clustering approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.     

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.     

Observability Analysis of Continuous‐Time LTI Systems with Limited Derivative Data

We consider continuous‐time LTI systems with either unknown‐input or with lack of information about the input and output derivatives. We compute the unknown‐input observability subspace and the observability subspace with unknown derivatives of input and output. We first formulate the unknown‐input observability subspace via projection matrices, then show that through having the unknown‐input observability subspace, one can easily evaluate the effect of known input and output signals but unknown derivatives on the observability subspace. Our method is demonstrated on the dynamics of a longitudinal aircraft in steady‐state flight.     

有界闭域上的线性赋值循环终止性分析

对有界闭域上的线性赋值循环程序终止性问题进行研究.利用Jordan 标准型技术将原循环程序的终止性问题约减为终止性等价的具有简单结构的循环程序的终止性问题.证明了当线性迭代映射满足一定条件时,该类循环程序不可终止的充分必要条件是：迭代映射在有界闭域上有不动点或周期轨.