A dilated LMI approach to robust performance analysis of linear time-invariant uncertain systems

This paper studies robust performance analysis problems of linear time-invariant systems affected by real parametric uncertainties. In the case where the state-space matrices of the system depend affinely on the uncertain parameters, it is know that recently developed extended or dilated linear matrix inequalities (LMIs) are effective to assess the robust performance in a less conservative fashion. This paper further extends those preceding results and propose a unified way to obtain numerically verifiable dilated LMI conditions even in the case of rational parameter dependence. In particular, it turns out that the proposed dilated LMIs enable us to assess the robust performance via multiaffine parameter-dependent Lyapunov variables so that less conservative analysis results can be achieved. Connections among the proposed conditions and existing results are also discussed concretely. Several existing results can be viewed as particular cases of the proposed conditions.     

Lagrange stabilisation for uncertain phase-controlled systems

In this article, we are concerned with the Lagrange stabilisation problem for phase-controlled systems with parameter uncertainties. By using the Kalman–Yakubovich–Popov lemma, the frequency-domain conditions for Lagrange stability of nominal phase-controlled systems are converted into linear matrix inequality (LMI) conditions. The allowable parameter uncertainty bounds for controllability and observability, as well as LMI conditions for Lagrange stability of uncertain phase-controlled systems, are presented. A controller design strategy based on the LMI method is proposed such that the uncertain phase-controlled systems are Lagrange stabilised. A numerical example is provided to demonstrate the effectiveness of the proposed results.     

参数辨识在陀螺仪性能分析中的应用

基于挠性陀螺仪动力学模型, 研究参数辨识在陀螺仪性能分析中的应用方法。在论述挠性陀螺仪性能指标和力学模型的基础上, 利用伪随机多谐波信号作为激励信号, 通过零阶保持器将离散模型转换为连续模型进行参数辨识。实验结果显示, 模型预测输出与实际测量数据的拟合度达到95. 3%, 残差在合理的公差带以内。因此, 参数辨识是陀螺仪性能分析中的一个有效性方法, 可为陀螺仪故障分析和控制回路设计提供一种技术手段和理论依据。     

A geometric approach to robust performance of parametric uncertain systems

A new approach for the robust performance problem for parametric uncertain systems is presented. Contrary to the classical approach, where specifications must be given in the frequency domain, this approach allows to deal with classical time specifications such as bounds on the overshoot, settling time and steady state error, which are matched to an uncertain reference model. Controller synthesis is then formulated as a set inclusion problem with a clear geometrical interpretation. Copyright © 2003 John Wiley & Sons, Ltd.     

一类参数不确定系统的鲁棒镇定

给出了一类参数不确定性系统的鲁棒镇定方法,首先对这类系统给出了一般描述,即这种系统的系统阵、输入阵都包含不确定参数,其非线性扰动项满足一定的约束条件.其次讨论了上述系统的基于代数黎卡提方程的正定解的鲁棒镇定方法.在应用方面,以包含静止同步补偿器(STATCOM)的电力系统为例,通过直接反馈线性化将上述系统转换为所讨论的线性系统.仿真结果验证了所提方法的控制效果.     

多变量系统的耦合梯度辨识算法与性能分析

针对多变量系统维数大、参数多、一般的辨识算法计算量大的问题, 基于耦合辨识概念, 推导多变量系统的耦合随机梯度算法, 利用鞅收敛定理分析算法的收敛性能. 算法的主要思想是将系统模型分解为多个单输出子系统,在子系统的递推辨识过程中, 将每个子系统的参数估计值耦合起来. 所提出算法与最小二乘算法和耦合最小二乘算法相比, 具有较少的计算量, 收敛速度可以通过引入遗忘因子得到改善. 性能分析表明了所提出算法收敛, 仿真实例验证了算法的有效性.

     

Hierarchical gradient-based identification of multivariable discrete-time systems

In this paper, we use a hierarchical identification principle to study identification problems for multivariable discrete-time systems. We propose a hierarchical gradient iterative algorithm and a hierarchical stochastic gradient algorithm and prove that the parameter estimation errors given by the algorithms converge to zero for any initial values under persistent excitation. The proposed algorithms can be applied to identification of systems involving non-stationary signals and have significant computational advantage over existing identification algorithms. Finally, we test the proposed algorithms by simulation and show their effectiveness.     

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。     

基于观测器的不确定T-S模糊系统鲁棒镇定

为带有参数不确定性的T-S模糊控制系统提出了新的基于观测器的鲁棒输出镇定条件. 该条件用来设计模糊控制器和模糊观测器. 为了设计模糊控制器和模糊观测器, 用T-S模糊模型来表示非线性系统, 并运用平行分布补偿观念. 充分条件基于二次Lyapunov函数, 通过将模糊系统的鲁棒镇定条件表述为一系列矩阵不等式, 比以往文献中列出的条件具有更小的保守性. 该不等式为双线性矩阵不等式, 可分两步骤先后解得使T-S模糊系统镇定的控制器增益和观测器增益. 最后, 通过对一个具有不确定性的连续时间非线性系统控制的例子证明了提出方法比以往方法更宽松.     

Local stability analysis and domain of attraction estimation for a class of uncertain nonlinear discrete‐time systems

This paper proposes a linear matrix inequality based method for the estimation of domain of attraction for a class of discrete‐time nonlinear systems subject to uncertain constant parameters. Recursive algebraic representations of the system dynamics and of the Lyapunov stability conditions are applied to obtain convex conditions which guarantee the system robust local stability while providing an estimate of the domain of attraction. A large class of discrete‐time nonlinear systems and of Lyapunov functions can be embedded in the proposed methodology including the whole class of regular rational functions of the system state variable and uncertain parameters. Numerical examples illustrate the application of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.     

不确定系统的不确定项观测器设计

为了克服常规用不确定项的界估计来设计控制器带来的保守性和动态性能差的弊端,本文就连续的和离散的不确定系统,分别提出了不确定项的观测器设计方案.通过引入分布和离散加权平均的概念,对这两种观测器的观测误差精度进行了分析估算,从数学上证明了所得的观测值在加权平均意义下是无偏的.此外,通过适当选择观测器反馈增益,可以得到满意的误差估计精度.把该方法应用到一个控制系统中,仿真结果表明所提出的观测器能很好的估计不确定项和改进闭环系统动态性能.     

Subspace identification of multivariable linear parameter-varying systems

A subspace identification method is discussed that deals with multivariable linear parameter-varying state-space systems with affine parameter dependence. It is shown that a major problem with subspace methods for this kind of system is the enormous dimension of the data matrices involved. To overcome the curse of dimensionality, we suggest using only the most dominant rows of the data matrices in estimating the model. An efficient selection algorithm is discussed that does not require the formation of the complete data matrices, but processes them row by row.     

小波神经网络在飞控系统辨识中的应用研究

选择以sigmoid函数为基础的小波基波函数构造了一个小波神经网络,利用小波网络对复杂的飞控系统对象进行在线辨识研究,仿真结果表明小波神经网络基本满足某型飞机飞控系统在线辨识的要求。     

不确定多变量系统的高阶滑模控制

针对一类系统矩阵和输入矩阵存在不确定性的多变量系统,结合微分估计器技术和极点配置技术,提出一种二阶非奇异终端滑模分解控制方法.所提方法适用于维数较高系统,可简化控制器设计,消除控制信号的高频抖振,实现系统的鲁棒递阶控制.仿真结果验证了所提方法的有效性.     

不确定随机非线性系统自适应观测器设计

考虑带非参数不确定项的随机非线性系统自适应观测器设计问题.不同于已有结果,系统的不确定项无需满足Lipschitz连续性条件,也不必要仅仅是系统输出的函数.通过设计一个带参数自适应律的非线性观测器来重构系统状态,该观测器结构简单目易于实现.应用Lyapunov稳定性理论和随机微分理论证明该观测器是最终有界的,并且它的界可以通过选取适当的参数进行调节.最后,数值仿真结果表明了该观测器的有效性.     

New strategy for robust stability analysis of discrete-time uncertain systems

In this paper, a new robust stability analysis approach is developed for uncertain discrete-time linear time-invariant systems with polytopic or affine parameter-dependent uncertainty models. The proposed approach is based on a combination of a branch-and-bound like strategy with linear matrix inequality (LMI) based analysis formulations. Two sufficient conditions are considered, one for the robust stability of the uncertain system and other one for the contrary situation. If both sufficient conditions fail to characterize the polytope, then it is iteratively subdivided into subpolytopes until some one proves to be unstable or all ones are verified to be robustly stable. The polytope subdivision is implemented by means of a specially developed simplex subdivision algorithm. Exhaustive numerical tests prove the efficiency of the proposed approach when compared with the most recent LMI-based formulations.     

Structural identification and software package for linear multivariable systems

This paper is concerned with the structural identification of linear multivariable systems and an interactive identification package. The structural identification is done by taking the time-invariant subsystem from the realizations of the input-output relations identified using data of disjoint time intervals, and the statistical hypothesis test is employed to determine the order, where the input-output relation is identified based on the generalized least squares method using the possibly larger model for the plant. The identification package is for the identification of the input-output relation of a linear multivariable system, for the structural identification based on the realization and for data management.     

Maximum likelihood‐based gradient estimation for multivariable nonlinear systems using the multiinnovation identification theory

This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is adopted to separate the parameters of the nonlinear block from the parameters of the linear block. By combining the model decomposition technique and the hierarchical identification principle, a key term separation‐based maximum likelihood recursive extended stochastic gradient algorithm with reduced computational complexity is presented to estimate all the parameters directly. By introducing the multiinnovation identification theory, a key term separation‐based maximum likelihood multiinnovation extended stochastic gradient algorithm is proposed to improve the parameter estimation accuracy. The simulation results illustrate the effectiveness of the proposed methods.     

Parameterization and identification of multivariable state-space systems: A canonical approach

In this paper, the problem of determining a canonical state-space representation for multivariable systems is revisited. A method is derived to build a canonical state-space representation directly from data generated by a linear time-invariant system. Contrary to the classic construction methods of canonical parameterizations, the technique developed in this paper does not assume the availability of any observability or controllability indices. However, it requires the -matrix of any minimal realization of the system to be non-derogatory. A subspace-based identification algorithm is also introduced to estimate such a canonical state-space parameterization directly from input–output data.