基于厚尾双学生氏t分布的非线性状态空间系统鲁棒辨识方法

状态空间模型作为一种常见且重要的模型结构在自动化领域有着广泛的应用,本文针对异常值干扰下的非线性状态空间系统辨识问题开展研究.与现有的辨识方法不同,本文充分考虑了状态转移过程和输出量测过程均受到异常值干扰的情况,提出了一种更加全面的鲁棒辨识算法.首先利用两个相互独立的学生氏t分布分别对状态噪声和输出噪声进行建模以保障算法的鲁棒性;其次利用粒子平滑算法估计状态变量的后验概率分布以解决状态未知问题;最后利用期望最大化算法实现未知参数估计.在算法实现过程中使用了学生氏t分布表达式的数学分解,这样做的好处是:(1)更加有利于算法的推导和实现;(2)更清晰地解释了算法的鲁棒性能.并且本文通过数值算例和应用算例验证了该方法的有效性.

基于IFO-KΔx的非线性状态反馈控制系统的几个设计概念

采用Hammerstein模型描述非线性系统时,可以应用增量式函数观测器IFO-K△x设计状态反馈控制系统,数学分析已证明,在不同的前提条件下,Luenberger函数观测器、全维状念观测器和增量式状态观测器均为增量式函数观测器的特例。该文从基于增量式函数观测器的状态反馈控制律作用下的非线性受控系统的闭环系统入手,导出几个重要概念,从而证明基于Hammerstein模型的非线性系统,应用增量式函数观测器设计状态反馈控制系统时,其控制器和观测 器可以独立地设计,即分离原理适用于该文所述之非线性控制系统,为IFO-K△x在热控领域的工程应用奠定理论基础。

一类含部分非线性状态系统的稳定条件

研究了一类非线性系统的稳定性问题 .利用一种正定连续可导的Lyapunov函数 ,证明了在系统平衡点附近全局渐近稳定的充分条件 .

改进的强跟踪求积分卡尔曼滤波算法

在非线性系统状态估计问题中,强跟踪求积分卡尔曼滤波算法在实现过程中由于对判断滤波发散的阈值设置较小,即使在系统正常情况下也会以较大概率产生渐消因子,从而导致过度调节滤波增益,使得系统状态估计不够平滑。针对该问题,提出了一种改进的强跟踪求积分卡尔曼滤波算法。该算法通过适当增大判断滤波发散的阈值,从而有效地降低了误判滤波发散的概率,增强了滤波器对系统状态的跟踪性能,并能够根据不同维数的量测方程确定弱化因子的取值,从而有效避免了凭经验选取弱化因子,具有较强的操作性。对两种算法进行实验仿真,结果表明,改进的强跟踪求积分卡尔曼滤波算法具有更高的滤波精度,减小了系统状态估计值与真实值之间的偏差。

扩展卡尔曼滤波的影响因素分析

介绍了扩展卡尔曼滤波算法流程,并对影响滤波效果的5个主要参数进行了系统的讨论,最后通过仿真实验方法研究不同的参数变化对于滤波的影响,并给出了参数的选择原则。

DESIGN OF NONLINEAR OBSERVER FOR NONLINEAR SYSTEM BASED ON RBF NEURAL NETWORKS

A new type of nonlinear observer for nonlinear systems is presented. Instead of approximating thc cntire nonlinear system with the neural network （NN）, only the un-modeled part left over after the lincarization is approximated. Compared with the conventional linear observer, the observer provides more accurate estimation of the state. The state estimation error is proved to asymptotically approach zero with the Lyapunov method. The simulation result shows that the proposed observer scheme is effective and has a potential application ability in the fault detection and identification （FDI）, and the state estimation.

Comparison of Linearized Kalman Filter and Extended Kalman Filter for Satellite Motion States Estimation

The performance of the conventional Kalman filter depends on process and measurement noise statistics given by the system model and measurements.The conventional Kalman filter is usually used for a linear system,but it should not be used for estimating the state of a nonlinear system such as a satellite motion because it is difficult to obtain the desired estimation results.The linearized Kalman filtering approach and the extended Kalman filtering approach have been proposed for a general nonlinear system.The equations of satellite motion are described.The satellite motion states are estimated,and the relevant estimation errors are calculated through the estimation algorithms of the both above mentioned approaches implemented in Matlab are estimated.The performances of the extended Kalman filter and the linearized Kalman filter are compared.The simulation results show that the extended Kalman filter is much better than the linearized Kalman filter at the aspect of estimation effect.

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.

Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process

State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modeled dynamics,parameter sensitivity,etc.This paper discusses the principles and characteristics of three different approaches,extended Kalman filters,strong tracking filters and unscented transformation based Kalman filters.By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance,an improved Kalman filter,unscented transformation based robust Kalman filter,is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process.The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.

Multi-sensor federated unscented Kalman filtering algorithm in intermittent observations

Aiming at the adverse effect caused by limited detecting probability of sensors on filtering preci- sion of a nonlinear system state, a novel muhi-sensor federated unscented Kalman filtering algorithm is proposed. Firstly, combined with the residual detection strategy, effective observations are cor- rectly identified. Secondly, according to the missing characteristic of observations and the structural feature of unscented Kalman filter, the iterative process of the single-sensor unscented Kalman filter in intermittent observations is given. The key idea is that the state estimation and its error covariance matrix are replaced by the state one-step prediction and its error covariance matrix, when the phe- nomenon of observations missing occurs. Finally, based on the realization mechanism of federated filter, a new fusion framework of state estimation from each local node is designed. And the filtering precision of system state is improved further by the effective management of observations missing and the rational utilization of redundancy and complementary information among multi-sensor observa- tions. The theory analysis and simulation results show the feasibility and effectiveness of the pro- posed algorithm.

Robust adaptive control for a class of uncertain non-affine nonlinear systems using neural state feedback compensation

A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.

Nonlinear parity space applied to an electric autonomous vehicle

The autonomous navigation of an electric vehicle requires the implementation of a number of sensors and actuators intended to inform it about his environment or his position and velocity and deliver necessary inputs. That＇s why it is important to detect and locate sensor and actuator faults as soon as possible to enable the operator to run the vehicle in degraded mode or use the fault tolerant control system if it exists. The main purpose of this paper deals with sensors or actuators faults diagnosis of autonomous vehicle. A diagnosis method using a nonlinear model of the vehicle is developed. Nonlinear state space model of the autonomous electric vehicle is used with the method of nonlinear analytical redundancy to detect and to isolate faults occurred on sensors or actuators. Computer simulations are carried out to verify the effectiveness of the method.

Software Package Evaluation for Lyapunov Exponent and Others Features of Signals Evaluating Condition Monitoring Performance of Nonlinear Dynamic Systems

Efficient use of industrial equipment, increase its availability, safety and economic issues spur strong research on maintenance programs based on their operating conditions. Machines normally operate in a linear range, but when malfunctions occur, nonlinear behavior might set in. By studying and comparing five nonlinear features, which listed in decreasing order by their damage detection capability are： LLE （largest Lyapunov exponent）, embedded dimension, Kappa determinism, time delay and cross error values; i.e., LLE performs best. Using somewhat similar ideas from Chaos control, i.e., vary the ＂mass imbalance＂ forcing parameters, we aim to stabilize the Lorenz equation. Quite interestingly, for certain imbalance excitation values, the system is stabilized. The previous even when paradigmatically chaotic parameters for Lorenz system are used （plus our forcing terms）. This quasi-control approach is validated studying signals obtained from the previously mentioned lab test. Finally, it is concluded that analyzing and comparing nonlinear features extracted from baseline vs. malfunction condition （test acquired）, one might increase the efficiency and the performance of machine condition monitoring.

STATE FEEDBACK STABILIZATION FOR A CLASS OF NONLINEAR TIME-DELAY SYSTEMS VIA DYNAMIC LINEAR CONTROLLERS

The dynamic linear state feedback control problem is addressed for a class of nonlinear systems subject to time-delay.First,using the dynamic change of coordinates,the problem of global state feedback stabilization is solved for a class of time-delay systems under a type of nonhomogeneous growth conditions.With the aid of an appropriate Lyapunov-Krasovskii functional and the adaptive strategy used in coordinates,the closed-loop system can be globally asymptotically stabilized by the dynamic linear state feedback controller.The growth condition in perturbations are more general than that in the existing results.The correctness of the theoretical results are illustrated with an academic simulation example.

A novel method of periodic orbit computation in circular restricted three-body problem

Periodic orbits are fundamental keys to understand the dynamical system of circular restricted three-body problem, and they play important roles in practical deep-space exploration. Current methods of periodic orbit computation need a high-order analytical approximate solution to start the iteration process, thus making the computation complicated and limiting the types of periodic orbits that can be obtained. By utilizing the symmetry of the restricted three-body problem, a special kind of flow function is constructed, so as to map a state on the plane of symmetry to another state that also lies in this plane. Based on this flow function, a new method of periodic orbit computation is derived. This method needs neither a starting analytic approximation nor the state transition matrix to be computed, so it can be conveniently implemented on a computer. Besides, this method is unaffected by the nonlinearity of the dynamical system, allowing a large set of periodic orbits which have x-z plane symmetry to be computed numerically. As examples, some planar periodic orbits (e.g. Lyapunov orbit) and spatial periodic orbits (e.g. Halo orbit) are computed. By further combining with a differential correction process, the method introduced here can be used to design resonant orbits that can jump between different resonant frequencies. One such resonant orbit is given in this paper, verifying the efficiency of this method.

Semi-Global Stabilization via Homogeneous Output Feedback for a Class of Nonlinear Time-Delay Systems

The semi-global stabilization problem for a class of nonlinear systems with state time-delay is addressed in this paper. By using Lyapunov-Krasovskii functional method and homogeneous dom- ination approach, a homogeneous observer and an output feedback controller with a scaling gain are designed. Then the sealing gain is adjusted such that the closed-loop system is semi-global asymptoti- cally stable. A numerical example is presented to illustrate the effectiveness of the obtained results in this paper.

H_∞ reference tracking control design for a class of nonlinear systems with time-varying delays

This paper investigates the H∞ trajectory tracking control for a class of nonlinear systems with time- varying delays by virtue of Lyapunov-Krasovskii stability theory and the linear matrix inequality （LMI） technique. A unified model consisting of a linear delayed dynamic system and a bounded static nonlinear operator is introduced, which covers most of the nonlinear systems with bounded nonlinear terms, such as the one-link robotic manipulator, chaotic systems, complex networks, the continuous stirred tank reactor （CSTR）, and the standard genetic regulatory network （SCRN）. First, the definition of the tracking control is given. Second, the H∞ performance analysis of the closed-loop system including this unified model, reference model, and state feedback controller is presented. Then criteria on the tracking controller design are derived in terms of LMIs such that the output of the closed-loop system tracks the given reference signal in the H∞ sense. The reference model adopted here is modified to be more flexible. A scaling factor is introduced to deal with the disturbance such that the control precision is improved. Finally, a CSTR system is provided to demonstrate the effectiveness of the established control laws.