基于平衡截断法的离散时间线性时滞系统的低频域模型降阶

研究在系统工作频率范围为低频区间情形下的离散时间线性时滞系统模型降阶问题. 首先根据时滞系统在零频点处传递函数的性质, 构造了一类匹配其零频响应的带有可调参数的扩展线性时滞系统, 进而在其基础上结合已有的时滞系统平衡截断法给出了新的低频模型降阶算法. 通过调节其中的设计参数, 新方法所得降阶时滞系统模型能够以任意精度逼近原系统的零频响应特性, 进而可以用于改善低频范围内的模型逼近性能. 数值算例验证了所提方法的有效性和优越性.     

模型的快速降阶技术

为快速进行模型的降阶,结合平衡截断（Balanced Truncation,BT）方法和特征正交分解（Proper Orthogonal Decomposition,POD）方法提出一种模型降阶方法.该方法采用频域POD快照矩阵低阶逼近系统的可控、可观Gram矩阵;通过奇异值分解（Singular Value Decomposition,SVD）提取BT＋POD模态,对低能量模态截断形成降阶子空间,并将其映射到全阶系统,从而形成基于状态空间的降阶模型（Reduced Order Model,ROM）;该模型就成为全阶模型（Full Order Model,FOM）的ROM.通过对阶数n=406的LTI SISO系统和阶数n=9的2区间电力系统进行的验证表明,在保留BT方法输入输出平衡特性的基础上,该方法效率高于BT方法.     

平衡系统的降阶准则

本文讨论平衡系统的模型简化问题,给出了一个新的降阶准则,改进了Moore的结果,一个数值例子说明了所给方法的优越性。     

大系统模型降阶的直接离散化方法

本文从频域最小二乘拟合出发,提出了高阶线性定常时间连续数学模型降阶为低阶线性定常时间离散模型的直接方法。本方法算法简单,便于编制计算机辅助设计程序,使降阶设计高度程序化;概念明确,在宽广的频带内具有较高的拟合精度,易为工程实际所应用。     

模型降阶方法研究

本文综合了模型降阶领域的现有方法,首先着重介绍了模型降阶的主要方法,并以图表的方式给出了各方法的分类及特性归纳。最后基于模型降阶的研究现状和存在的问题提出了一些看法,以便为实际工程应用提供参考。     

基于模型降阶的分数阶系统动态性能研究

由于大多数分数阶系统阶次过高,使得系统的控制器设计变得非常困难,会造成系统控制精度变差且动态性能降低等不利因素,而模型降阶技术是解决这一问题的有效工具.首先简要介绍了H2范数模型降阶的一般方法,并提出一种新的降阶模型结构,可以使降阶后的模型扩展到分数阶并且更加精确地逼近各种高阶系统.仿真结果证明,采用改进型H2范数模型降阶方法不仅保持了原有分数阶模型系统的动态性能而且还提升了原有整数阶模型系统的动态性能.     

基于平衡截断方法的高超声速飞行器模型降阶

本文提出了一种适用于高超声速飞行器数学模型的模型降阶方法.该方法采用基于奇异值分解的投影技术,对高阶的高超声速飞行器数学模型进行平衡变换,进而通过截断获得低阶的微分方程,方便控制器的设计,达到模型降阶的目的.相比于传统的只适用于稳定系统的模型降阶方法,本文提出的模型降阶方法通过右互质分解可以应用于不稳定的系统的模型降阶.为了证明该模型降阶方法的准确性,本文通过平均灰色关联系数的计算,给出基于时域分析的定量验证结果.仿真部分成功应用该方法对美国空军实验室提出的弹性纵向模型实现了降阶,证明了该方法的有效性.     

离散时间系统Hankel最优模型降阶及其应用

针对离散时间系统Hankel最优模型降阶问题,对标量情况由最优Hankel范数近似给出了状态空间解。将这一理论应用于无限脉冲响应（IIR）滤波器的设计,进一步提出了一种基于最优Hankel范数近似的线性相位IIR滤波器设计方法。通过采用线性相位FIR滤波器和最优Hankel范数近似方法设计IIR滤波器,使其具有线性相位,减小了逆矩阵求解中的计算量。低通滤波器的仿真实例验证了该方法的有效性。     

关于模型降阶的一些试验结果

把几种具有较高逼近精度的频域降阶模型用于控制系统设计,给出设计后的仿真结果.说明不仅应研究如何提高逼近精度,而且应研究降阶设计方法.     

Coprime factors reduction methods for linear parameter varying and uncertain systems

We present a generalization of the coprime factors model reduction method of Meyer and propose a balanced truncation reduction algorithm for a class of systems containing linear parameter varying and uncertain system models. A complete derivation of coprime factorizations for this class of systems is also given. The reduction method proposed is thus applicable to linear parameter varying and uncertain system realizations that do not satisfy the structured ℓ₂-induced stability constraint required in the standard nonfactored case. Reduction error bounds in the ℓ₂-induced norm of the factorized mapping are given.     

LPV model development and control of a solution copolymerization reactor

In this paper, linear parameter-varying (LPV) control is considered for a solution copolymerization reactor, which takes into account the time-varying nature of the parameters of the process. The nonlinear model of the process is first converted to an exact LPV model representation in the state-space form that has a large number of scheduling variables and hence is not appropriate for control design purposes due to the complexity of the LPV control synthesis problem. To reduce such complexity, two approaches are proposed in this paper. First, an approximate LPV representation with only one scheduling variable is obtained by means of a parameter set mapping (PSM). The second approach is based on reformulating the nonlinear model so that it provides an LPV model with a fewer number of scheduling parameters but preserves the same input–output behavior. Moreover, in the implementation of the LPV controllers synthesized with the derived models, the unmeasurable scheduling variables are estimated by an extended Kalman filter. Simulation results using the nonlinear model of the copolymerization reactor are provided in order to illustrate the performance of the proposed controllers in reducing the convergence time and the control effort.     

Coprime factors reduction of distributed nonstationary LPV systems

This paper is on the coprime factors reduction of distributed systems formed by discrete-time, heterogeneous, nonstationary linear parameter-varying subsystems. The subsystems are represented in a linear fractional transformation framework and interconnected over arbitrary directed graphs, and the communication between the subsystems is subjected to a delay of one time-step. Two methods for constructing a contractive coprime factorisation for the full-order system are proposed. This factorisation forms an augmented system which is reducible by the structure-preserving balanced truncation method. A reduced-order contractive coprime factorisation is obtained from which the reduced-order system can be formed. A robustness theorem is also provided to interpret the error bound from coprime factors reduction in terms of robust stability of the closed-loop system. A numerical example is considered at the end of the paper.     

Simultaneous sensor and actuator fault estimation for continuous-time polytopic LPV system

In this paper, we study the problem of state estimation and both actuator and sensor fault detection for Linear Polytopic Parameter-Varying (LPV) system. The contribution of this work consists on the design of a novel robust adaptive observer based on polyquadratic formulations with a new set of relaxation. An optimisation problem is given in term of Linear Matrix Inequalities (LMI) in order to guarantee the stability of the system and the asymptotic convergence of faults error. A comparative study is made to prove the efficiency of the proposed polyquadratic algorithms against the quadratic ones. The performances and effectiveness of the proposed methods are illustrated in a simulation example where constant and variable actuator and sensor faults were detected.     

A frequency weighted model order reduction technique and error bounds

A new frequency weighted technique for balanced model reduction is proposed. The proposed technique not only provides stable reduced order models for the case when both input and output weightings are included but also yields frequency response error bounds. The method is illustrated using numerical examples and the results are compared with other frequency weighted model reduction techniques.     

具有时变时延的网络化LPV系统的容错控制

针对一类具有时变时延以及Lipschitz非线性项的网络化线性参数变化系统,研究了系统中存在外部扰动、执行器和传感器同时发生随机故障时的容错控制问题。用Bernoulli分布序列描述执行器和传感器发生的随机故障,利用自由权矩阵方法处理时变时延。根据Lyapunov-Krasovskii稳定性定理和线性矩阵不等式（LMI）方法求出◢H◣▼∞▽容错控制器存在的充分条件,然后通过利用近似基函数和网格化技术将无限维的LMI求解问题转换为有限维的LMI问题,得到了相应的容错控制器增益。最后,通过数值仿真验证了所设计方法的有效性。     

Bridging the gap between theory and practice in LPV fault detection for flight control actuators

Two different approaches for fault detection, the geometric and the detection filter based methods, are compared in the paper from practical aspects, using the linear parameter-varying (LPV) framework. Presenting two designs allows a comparison of global, system level, and local component level fault detection methods with special emphasis on their relevance to aircraft industry. Practical engineering design decisions are highlighted via applying them to a high-fidelity commercial aircraft problem. The successive steps of the design, including fault modeling, LPV model generation, and LPV FDI filter synthesis, including implementation aspects, are discussed. Results are presented according to the industrial assessment perspectives phrased within the EU ADDSAFE project.     

LPV模型的Kalman滤波器设计

针对线性变参数（LPV）模型,给出了一种改进Kalman滤波器设计方法,这种滤波方法可以根据系统LPV模型实时调整滤波状态方程矩阵系数,从而增强滤波算法对系统模型变化的跟踪性能,并提高滤波准确性。结合具体实例,通过仿真比较,较好地检验了该方法的有效性。     

On the balanced truncation and coprime factors reduction of Markovian jump linear systems

The paper deals with the balanced truncation and coprime factors reduction of Markovian jump linear (MJL) systems, which can have mode-varying state, input, and output dimensions. We develop machinery for balancing mean square stable MJL system realizations using generalized Gramians and strict Lyapunov inequalities, and provide an improved a priori upper bound on the error induced in the balanced truncation process. We also generalize the coprime factors reduction method and, in doing so, extend the applicability of the balanced truncation technique to the class of mean square stabilizable and detectable MJL systems. We provide tools to establish mean square stabilizability and detectability of the considered MJL systems. In addition, a notion of right-coprime factorization of MJL systems and methods to construct such factorizations are given. The error measure in the coprime factors reduction approach, while still norm-based, does not directly capture the mismatch between the nominal system and the reduced-order model, as is the case in the balanced truncation approach where mean square stable models are considered. Instead, the error measure is given in terms of the distance between the coprime factors realizations, and thus has an interpretation in terms of robust feedback stability. The paper concludes with an illustrative example which demonstrates how to apply the coprime factors model reduction approach.     

Gain-scheduled, model-based anti-windup for LPV systems

The aim of this paper is to show that a recently proposed technique for anti-windup control of exponentially unstable plants can be easily extended to solve the corresponding robust anti-windup problem for linear parameter varying systems, for which the time varying parameters are measured online. The proposed technique is minimally conservative with respect to the size of the resulting operating region (which coincides, up to an arbitrarily small quantity, with the largest set on which asymptotic stability can be guaranteed for the considered plant with the given saturation level and uncertainty characteristics), and is not limited to plants having only small uncertainties or being open-loop stable.