Adaptive Observer for a Class of Nonlinear Systems

这份报纸调查工具与 Lipschitz 条件和未知参数为非线性的系统的一个类设计观察员。完整顺序的州的适应观察员的一条新设计途径被建议。构造观察员能保证状态的错误和参数评价的错误 asymptotically 收敛到零。而且，一个数字例子被提供验证观察员的有效性。

Adaptive Observer-Based Fault Estimate for Nonlinear Systems

An approach for adaptive observer-based fault estimate for nonlinear system is proposed.H-infinity theory is applied to analyzing the design method and stable conditions of the adaptive observer, from which both system state and fault can be estimated. It is proved that the fault estimate error is related to the given H-infinity track performance indexes,as well as to the changing rate of the fault and the Lipschitz constant of the nonlinear item.The design steps of the adaptive observer are proposed.The simulation results show that the observer has good performance for fault estimate even when the system includes nonlinear terms, which confirms the effectiveness of the method.

Adaptive Fuzzy Observer Backstepping Control for a Class of Uncertain Nonlinear Systems with Unknown Time-delay

In this paper, a new adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time-delay and unmeasured states. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed for estimating the unmeasured states. On the basis of the state observer and applying the backstepping technique, an adaptive fuzzy observer control approach is developed. The main features of the proposed adaptive fuzzy control approach not only guarantees that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded, but also contain less adaptation parameters to be updated on-line. Finally, simulation results are provided to show the effectiveness of the proposed approach.

Fault diagnosis based on high-gain observer with an update law for a class of nonlinear systems

In this paper,by using the well-known high-gain observer design,an update law for the gain and an adaptive estimation of parameters,a new method of fault diagnosis for a class of nonlinear systems is presented.Without resort to any transformation for the parameters,the estimation errors of the states and the parameters are guaranteed to be globally exponentially convergent by a persistent excitation condition.Compared to the existing results,it can be applied to nonlinear systems with nonlinear terms admitting an incremental rate depending on the measured output.A case study further verifies the validity of the proposed research.

Soft Sensor for Inputs and Parameters Using Nonlinear Singular State Observer in Chemical Processes

Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.

Command filtered integrated estimation guidance and control for strapdown missiles with circular field of view

In this paper,an integrated estimation guidance and control(IEGC)system is designed based on the command filtered backstepping approach for circular field-of-view(FOV)strapdown missiles.The threedimensional integrated estimation guidance and control nonlinear model with limited actuator deflection angle is established considering the seeker's FOV constraint.The boundary time-varying integral barrier Lyapunov function(IBLF)is employed in backstepping design to constrain the body line-of-sight(BLOS)in IEGC system to fit a circular FOV.Then,the nonlinear adaptive controller is designed to estimate the changing aerodynamic parameters.The generalized extended state observer(GESO)is designed to estimate the acceleration of the maneuvering targets and the unmatched time-varying disturbances for improving tracking accuracy.Furthermore,the command filters are used to solve the"differential expansion"problem during the backstepping design.The Lyapunov theory is used to prove the stability of the overall closed-loop IEGC system.Finally,the simulation results validate the integrated system's effectiveness,achieving high accuracy strikes against maneuvering targets.

Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks

In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.

非线性互联系统状态观测器设计方法研究

非线性互联系统广泛应用于各类工业生产中。随着科学技术的迅速发展,控制系统的复杂程度越来越高,同时各子系统之间的互联性也变得越来越强。因此,针对日益复杂的非线性互联系统进行研究,已成为一个非常重要的课题。针对非线性互联系统观测器的设计方法进行研究,通过设计状态观测器实现对互联系统中状态向量的鲁棒估计。首先,基于李雅普诺夫函数给出广义互联系统状态观测器存在的充分条件。其次,将非线性广义互联系统观测器的存在条件进行推广,得到正常非线性互联系统观测器存在的充分条件。观测器的存在条件均通过Schur补引理,以线性矩阵不等式的形式给出,简化了观测器增益矩阵的求解,得到了互联系统观测器的设计方法。最后,利用MATLAB软件计算出观测器的增益矩阵。两个算例仿真结果验证了所提方法的可行性与有效性。

实用NARMAX-L2模型的神经网络自校正控制器

针对实用随机非线性NARMAX模型,参照改进的NARMA-L2模型,提出实用随机NARMAX-L2模型,采用BP神经网络逼近该模型,依据直接极小化指标函数自适应优化算法估计随机干扰模型的参数,并对BP神经网络的连接权进行学习,根据广义控制目标函数提出神经网络显式自校正控制器,仿真研究验证了上述研究的有效性,系统具有理想的控制效果。

机车PMSM牵引传动系统自适应模糊反推滑模控制

为研究电力机车PMSM牵引传动系统带载运行时的转速控制精度,考虑电机转子轴和负载轴所受扰动,建立了PMSM牵引传动系统的数学模型;针对系统中存在的时变扰动,依据非线性观测器设计理论构造非线性扰动观测器对其实际值进行估计,并对估计误差进行自适应模糊逼近;依据Lyapunov稳定性理论,基于微分估计器能够任意精度估计的优点,构造了基于微分估计器的自适应模糊反推滑模控制器。理论分析及仿真结果表明,PMSM牵引传动系统闭环跟踪误差一致有界,扰动观测误差收敛于0。

非凸投影自适应Hammerstein滤波算法

该文研究了Hammerstein系统参数辨识和非线性系统预测问题,提出一种基于非凸投影的自适应滤波算法。论文将问题归结为具有非凸可行域的约束优化问题,并建立了基于交替方向乘子法(ADMM)和递归最小二乘相结合的算法框架。在该算法框架下,非凸约束优化问题的全局最优解可通过岭回归和欧几里得(Euclid)投影循环计算得到。将提出的算法分别应用于Hammerstein系统的参数辨识、非线性未知系统预测以及非线性声学回声消除,并进行仿真实验,结果显示所提算法具有较好的收敛性和稳定性,能够得到较准确的辨识和预测效果。

改进的蝗虫优化算法及其应用

针对蝗虫优化算法(GOA)收敛精度不高、易陷入局部最优等问题,提出一种非线性自适应模拟退火的蝗虫优化算法(SA-NGOA)。利用非线性自适应更新GOA的关键参数,更好地协调算法的全局探索和局部开发;利用模拟退火算法,以一定概率接收GOA的劣势解,提高算法的收敛精度。实验结果表明,SA-NGOA算法具有良好的优化性能。同时,将SA-NGOA算法用于扩展同步参数观测器(ESPO)参数优化,其结果表明,该算法优化后的ESPO具有更快的辨识速度和更稳定的精度。

ADAPTIVE TRACKING OF A CLASS OF FIRST-ORDER SYSTEMS WITH BINARY-VALUED OBSERVATIONS AND FIXED THRESHOLDS

这份报纸为一阶的系统的一个班考虑适应追踪问题，珍视二进制代码的观察经由固定阀值产生了。一个递归的设计算法基于系统噪音的统计性质为参数评价被建议。然后，一条适应控制法律经由必然等价原则被设计。由关于估计的革新和输出预言的有条件的期望的使用，靠近环的系统被显示稳定、 asymptotically 最佳。同时，参数估计被证明肯定几乎是两个并且意味着平方会聚，并且评价错误的集中率也被获得。一个数字例子被给表明适应控制法律的效率。

Robust state of charge and state of health estimation for batteries using a novel multi model approach

Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system(BMS).Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system.We propose a new robust method,called ERMES(extendible range multi-model estimator),for determining an estimated state-of-charge(SoC),an estimated state-of-health(SoH)and a prediction of uncertainty of the estimates(state-of-uncertainty—SoU),thanks to which it is possible to monitor the validity of the estimates and adjust it,extending the robustness against a wider range of uncertainty,if necessary.Specifically,a finite number of models in state-space form are considered starting from a modified Thevenin battery model.Each model is characterized by a hypothesis of SoH value.An iterated extended Kalman filter(EKF)is then applied to each model in parallel,estimating for each one the SoC state variable.Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF,yielding the overall SoC and SoH estimates,respectively.In addition,a figure of uncertainty of such estimates is also provided.

基于自适应补偿的异步电机静止参数辨识方法

在异步电机调速系统中,电机参数的精度直接影响着系统的控制性能。为了获得准确的电机参数,提出了一种异步电机在静止状态下的参数辨识方法。采用了一种自适应的方法对器件的非线性进行补偿,从而提高了算法的辨识精度和鲁棒性。考虑到电机的磁滞效应对电机互感的影响,在传统单相交流实验的基础上叠加1个直流电流,利用此直流电流先将电机磁场激励起来,从而准确地辨识出电机在额定励磁情况下的互感值。最后实验证明了此方法可以准确辨识出电机的全部参数。

一类非线性系统的自适应观测器设计

在故障诊断应用中,状态方程中的未知参数和输出方程中的未知参数分别表征执行机构故障和传感器故障,所以研究状态方程和输出方程同时含有未知参数的自适应观测器有着实际的应用意义.本文基于高增益观测器和自适应估计理论,针对状态方程和输出方程同时含有未知参数的一类一致可观的非线性系统,用构造性方法设计了一种联合估计状态和未知参数的自适应观测器.该自适应观测器的参数估计采用时变增益矩阵,结构形式及参数设置简单.给出了使该自适应观测器满足全局指数收敛性的持续激励条件,并在理论上简洁地证明了该自适应观测器的全局指数收敛性.数值仿真结果表明该自适应观测器具有良好的快速收敛性、跟踪性等期望性能.

基于状态估计的风电机组液压变桨距系统故障检测

液压变桨距系统是风电机组的高频故障部件之一,对其进行早期故障检测,可以有效地提高风电机组运行的可靠性,减少不必要的经济损失。风电机组所受环境因素干扰较多,而基于数据的故障检测方法易受这些因素的影响造成误报警,并且故障分离较为困难。因此,本文提出基于状态估计的方法对风电机组液压变桨距系统进行故障检测。首先,构建风电机组液压变桨距系统的状态空间模型及其状态观测器,利用液压变桨距系统模型的实际输出与状态观测器输出产生残差;其次,将残差的均方根作为残差估计函数,并配合自适应阈值进行决策。最后,以液压变桨距执行机构故障和节距角传感器故障为例,通过仿真验证了该方法的有效性。

车辆行驶过程中的状态估计问题综述

从传感器配置、估计用物理模型、状态估计算法和估计过程中的模型参数自适应4个方面回顾车辆行驶过程中的状态估计问题。对比分析以纵向车速、横摆角速度、质心侧偏角为估计目标时传感器的常见配置,给出合理的传感器配置方案;比较在不同估计目标下运动学模型和动力学模型的优缺点,提出纵向车速和横摆角速度适合采用运动学模型、质心侧偏角估计适合采用动力学模型的观点;列举并比较车辆状态估计中常用的估计算法,给出各算法在实际应用中所需注意的因素。指出实现估计过程中的参数自适应是提高不同行驶工况下观测器估计精度的有效手段,并介绍递推最小二乘、联合卡尔曼滤波等实现参数自适应的典型方法。

基于参数估计误差的鲁棒自适应律设计及验证

针对传统自适应控制系统设计的自适应律参数收敛慢进而影响控制系统瞬态性能的问题,研究一类新的基于参数估计误差修正的鲁棒自适应律设计.首先引入滤波操作给出参数估计误差的提取方法,构建出含参数估计误差修正项的自适应律,进而将该自适应律用于控制器设计和分析中,可同时实现控制误差和参数估计误差指数收敛.对比分析了几类传统自适应律和所提出自适应律的收敛性和鲁棒性,并给出了保证参数收敛所需持续激励条件的一种直观、简便的在线判别方法.数值仿真及基于自制三自由度直升机系统俯仰轴实验结果表明,基于参数误差修正的自适应律及控制器可得到优于传统自适应方法的跟踪控制和参数估计性能.

WAMS中计及量测函数非线性项的电力系统自适应动态状态估计

提出两种在线辨识和修正模型误差协方差矩阵的自适应动态状态估计方法,并在这两种状态估计模型中引入量测函数的非线性项。这不仅在进行状态滤波的同时,利用观测数据提供的信息,在线辨识和估计系统中未知或不精确的噪声统计特性,而且由于计及了量测函数的非线性项,非线性系统在线性化过程中所产生的线性化误差完全得到补偿,极大地提高了算法在异常运行情况下的自适应性;并且在量测量中计入了相量测量单元(PMU)测量的电压幅值和相角,增加了系统的冗余量测,提高了滤波精度。仿真结果验证了所提方法在正常情况、负荷突变、存在坏数据、网络拓扑结构错误各种情况下具有较好的预测和滤波效果。