局部加权组合状态空间系统正交梯度辨识

提出了局部加权组合状态空问模型参数的正交梯度辨识方法.局部状态空间模型用全参数化形式描述,同时选用正则化径向基函数作为分状态的加权因子.通过优化系统输出误差得到了系统矩阵、径向基函数中心与带宽的参数估计.仿真结果表明,所提出的方法用于非线性动态系统的建模是有效的.     

局部加权组合状态空间系统正交梯度辨识

提出了局部加权组合状态空间模型参数的正交梯度辨识方法.局部状态空间模型用全参数化形式描述,同时选用正则化径向基函数作为分状态的加权因子.通过优化系统输出误差得到了系统矩阵,径向基函数中心与带宽的参数估计.仿真结果表明,所提出的方法用于非线性动态系统的建模是有效的.

     

基于神经网络的非线性时间序列故障预报

对模型未知非线性系统, 将系统输出组成时间序列并通过空间嵌入的方法转化为一个离散动态系统. 利用线性 AR 模型拟合时间序列的线性部分, 用神经网络拟合时间序列的非线性部分并补偿外界未知的扰动, 提出了通过对状态的观测实现时间序列一步预测的方法. 利用滚动优化的思想将一步预测推广, 提出了时间序列的 N 步预测方法, 证明了时间序列预测误差有界. 通过对预测误差进行概率密度估计和检验, 提出了故障的预报方法. 对 F-16 歼击机的结构故障预报结果表明了方法的有效性.     

增量式参数化特征模型及其单目视觉导航

为了解决单目摄像机6自由度运动参数估计中的尺度不确定问题,提出了场景特征深度的“增量式参数化”模型.和传统的欧几里得参数化模型和逆深度参数化模型不同,在该模型中,图像序列特征点仅仅由其深度特征进行增量式描述.此外,引入了非线性系统的状态可观测性理论,对不同参数化模型进行了可观测性分析,证明了“增量式参数化模型”的一致可观测性.最后采用室内移动摄像机采集的公共数据集,进行了室内单目视觉导航实验,实验结果证明了该增量式参数化模型能够显著改善单目视觉导航性能.     

态空间系统的梯度优化辨识及收敛性分析*

为解决状态空间系统的预报误差与系统参数之间的非线性、非凸性给参数估计带来的困难,提出了状态空间系统的梯度优化辨识方法。分析了基于局部线性化的梯度辨识原理,给出了基于QR分解、奇异值分解(SVD)确定参数搜索方向的实现方案,得到了估计系统参数的迭代辨识算法。探讨了算法的收敛性、给出了算法收敛速度的解析表达式,最后进行了数值仿真,实验结果说明了所提出方法的有效性。     

考虑控制饱和的连铸结晶器振动位移系统预设性能控制

针对伺服电机驱动的连铸结晶器控制系统执行器输入饱和和状态受限问题,同时考虑系统存在负载扰动、参数摄动等不确定性问题,提出一种基于扩张状态观测器的跟踪误差预设性能反步控制策略.首先,针对执行器输入饱和问题,建立系统的数学模型;然后,采用一种线性扩张状态观测器实时观测系统时变负载扰动、参数摄动等不确定性,并对观测误差的收敛性进行分析;接着,针对伺服电机电流饱和与跟踪误差预设性能控制问题,通过引入辅助状态变量确保系统跟踪误差限定在允许范围内,设计基于扩张状态观测器的反步(Backstepping)控制器;最后,根据Lyapunov稳定性理论证明闭环系统的稳定性,并通过系统仿真验证所提出控制策略的有效性.     

随机状态空间系统的梯度优化辨识

提出了随机状态空间系统参数的梯度优化辨识方法。通过极小化输出预报误差而获得系统的参数估计。提出了动态选择雅可比矩阵奇异值比率确定参数搜索方向的方法,用以解决因雅可比矩阵的线性相关性引起的算法失效问题。给出了融合参数局部逼近性能信息的辨识算法,并得到了算法收敛速度的解析表达式。数值仿真实验的结果说明了所提出方法的有效性。     

基于有限时间输出反馈的线性扩张状态观测器

为快速、准确地观测系统中的未知扰动及状态, 提出一种有限时间线性扩张状态观测器(Finite-time linear extended state observer, FT-LESO), 它具有期望的收敛性能且结构简单、易于设计. 假设系统的状态无法量测, 观测器设计问题转化为扰动下的输出反馈控制问题. 针对该问题, 提出一种扰动下的有限时间线性输出反馈控制方法, 得到控制器参数与闭环系统状态向量2-范数间的解析关系. 在此基础上, 提出有限时间线性扩张状态观测器, 得到观测器参数与观测误差收敛速度及稳态观测误差间的解析关系, 给出一充分条件保证观测误差有限时间有界、且能以不低于指数收敛的速度收敛到给定范围内, 为观测器参数设计提供理论依据. 通过数值仿真验证提出的观测器, 仿真结果与理论分析相符, 提出的观测器是有效的.     

空间光通信中轨道短时预报

瞄准、捕获和跟踪(PAT)技术是星间光通信的关键技术之一,高精度卫星轨道短时预报能有效实现PAT.首先在EGM96地球引力场模型下建立了卫星状态动力方程和预报方程,然后改进基于数值算法的扩展卡尔曼滤波算法对卫星轨道短时预报,最后以champ卫星星载GPS实时定轨数据为卡尔曼滤波器观测数据进行仿真实验:预报卫星位置误差约亚米级,速度误差约0.05m/s;卫星位置和速度的均方差估计趋于稳定,在一定程度上能很好地克服离散误差和模型误差对轨道估计精度的影响.PAT瞄准精度约1.6 μrad.预报轨道精度能满足空间光通信PAT技术要求.     

动态系统预报的一种新方法

本文给出了关于动态系统预报的一种新方法.这种方法把状态预报分成两个部分,即动 态系统参数的预报和以参数的预报值为基础的状态预报.误差分析与数值实例说明该方法可 以大大提高预报精度.     

基于正交梯度搜索的动态系统递阶优化辨识

提出了一种辨识线性时不变状态空间系统参数的正交梯度二步递阶优化方法. 通过极小化输出误差目标函数获得了系统参数估计; 提出了正交梯度搜索方法用于解决系统参数的非唯一性问题, 正交梯度搜索的本质是在输入-输出等价类相切平面的正交垂空间更新系统参数; 给出了用 L-M 算法进行参数优化的充分条件; 提出的系统参数递阶优化辨识方法包括两步: 首先用给出的自适应 L-M 算子正交梯度方法确定参数优化方向; 其次由一维搜索方法计算最佳步长. 蒙特卡罗数值仿真实验表明本文提出的方法具有收敛速度快、抗噪能力强以及数值稳定性好等优点.     

Active fault tolerant control using state-space self-tuning control approach

This paper presents a new fault tolerant control scheme for unknown multivariable stochastic systems by modifying the conventional state-space self-tuning control approach. For the detection of faults, a quantitative criterion is developed by comparing the innovation process errors occurring in the Kalman filter estimation algorithm, which, for faulty system recovery, a weighting matrix resetting technique is developed by adjusting and resetting the covariance matrices of the parameter estimate obtained in the Kalman filter estimation algorithm to improve the parameter estimation of the faulty systems. The proposed method can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection. The modified state-space self-tuning control scheme can be applied to the multivariable stochastic faulty system without requiring prior knowledge of system parameters and noise properties.     

Robust stability of state-space models with structureduncertainties

A method for robust eigenvalue location analysis of linear state-space models affected by structured real parametric perturbations is proposed. The approach, based on algebraic matrix properties, deals with state-space models in which system matrix entries are perturbed by polynomial functions of a set of uncertain physical parameters. A method converting the robust stability problem into nonsingularity analysis of a suitable matrix is proposed. The method requires a check of the positivity of a multinomial form over a hyperrectangular domain in parameter space. This problem, which can be reduced to finding the real solutions of a system of polynomial equations, simplifies considerably when cases with one or two uncertain parameters are considered. For these cases, necessary and sufficient conditions for stability are given in terms of the solution of suitable real eigenvalue problems     

Parameter identification of continuous-time systems using iterative learning control

This paper presents a system identification technique for continuous-time state-space system using the iterative learning control. The transfer function parameters are regarded as functions with respect to the state-space parameters which will be identified. The relationship between the state-space parameters and the response error is explicitly derived. An update law of the state-space parameters is proposed so as to improve the convergence speed. The effectiveness of the proposed identification technique is demonstrated by numerical examples.     

Robust control with structure perturbations

The problem of making a given stabilizing controller robust so that the closed-loop system remains stable for prescribed ranges of variations of a set of physical parameters in the plant. The problem is treated in the state-space and transfer-function domains. In the state-space domain a stability hypersphere is determined in the parameter space using Lyapunov theory. The radius of this hypersphere is iteratively increased by adjusting the controller parameters until the prescribed perturbation ranges are contained in the stability hypersphere. In the transfer-function domain a corresponding stability margin is defined and optimized on the basis of the recently introduced concept of the largest stability hypersphere in the space of coefficients of the closed-loop characteristic polynomial. The design algorithms are illustrated by examples     

Decoupling of structured systems by parameter-independentprecompensation and state feedback

In this paper, structured systems described by state-space models are considered. For these systems, the entries of the state-space model matrices are assumed to be either fixed zeros or free independent parameters. We study the dynamic decoupling of structured linear systems. It turns out that a large class of systems which is not state feedback decouplable can be made decouplable by the addition of a simple structured precompensator. Such a precompensator then has the property to be independent of the system parameters. We give a necessary and sufficient condition for a structured system to be made feedback decouplable using a parameter independent precompensator. Furthermore, we prove that when such a solution exists, the precompensator can be chosen to be diagonal. In this case the precompensator is constructed via a simple combinatorial algorithm. Our approach is based on a graph representation of the structured system. All the results of this paper are related to some particular shortest sets of input-output paths in the graph associated with the structured system     

一类MIMO系统连续状态空间模型的参数辨识频域方法

在连续时间状态空间模型的参数辨识中,针对系统状态微分项获取困难这一问题,对输入、状态及输出序列应用离散傅里叶变换,得到复数域线性回归方程,并给出了不同形式的最小二乘解估计式.以飞行器多输入多输出(Multiple-input multiple-output, MIMO)状态空间模型为例,设计正交多正弦信号对系统进行多通道同时激励,在一次激励的情况下就可以辨识出所有模型参数,从而提高辨识实验效率.仿真实验证明了方法的有效性和结果的准确性.     

Exploiting equivalence reduction and the sweep-line method for detecting terminal states

State-space exploration is one of the main approaches to computer-aided verification and analysis of finite-state systems. It is used to reason about a wide range of properties during the design phase of a system, including system deadlocks. Unfortunately, state-space exploration needs to handle huge state spaces for most practical systems. Several state-space reduction methods have been developed to tackle this problem. In this paper, we develop algorithms for combining two of these methods: state equivalence class reduction and the sweep-line. The algorithms allow deadlocks to be detected by recording terminal states of the system on-the-fly during state-space exploration. We derive expressions for the complexity of the algorithms and demonstrate their usefulness with an industrial case study. Our results show that the combined method achieves at least a six-fold reduction of the state space for interesting parameter values compared with either method used in isolation while still proving the desired system property of the terminal states. The runtime performance of the combined method is almost the same as that of the equivalence class method over the chosen parameter range. Moreover, the improvement in space reduction increases with increased parameter values.