非均匀采样系统的一种递推辨识方法

在非均匀采样系统辨识方法中, 通常利用重采样、数值积分等方法来处理非均匀采样数据, 所用模型多为连续有理分式传递函数, 在递推形式下非均匀采样对象又常局限于``数据缺失'的情况. 本文研究更为一般的异步非均匀采样的多变量系统, 采用连续时间状态空间模型描述, 推导了模型参数、参数梯度和系统状态之间的相互递推关系, 构成一种可变迭代间隔的递推辨识算法, 在每次输出采样点上仅更新模型中受当前采样数据影响的参数. 这种辨识方法可以适用于任意非均匀采样系统, 多采样率系统也可作为一种特例适用于本算法. 仿真结果表明, 所提的方法是可行有效的.     

分数阶时滞系统的频域子空间辨识

研究了利用频率响应数据辨识分数阶时滞系统子空间模型的问题,给出了一种差分进化算法与频域子空间方法相结合的辨识算法.利用差分进化算法搜索最优分数微分阶次和时滞参数,而对于固定的分数微分阶次和时滞,则采用分数阶频域子空间辨识方法得到状态空间模型.通过仿真算例验证了该算法的有效性.     

非均匀采样数据系统的一种辨识方法

阐述了非均匀采样方案,推导了非均匀多率采样系统的状态空间模型,进一步获得了对应的传递函数模型.为解决辨识模型信息向量中存在未知变量的问题,使用辅助模型技术,用辅助模型的输出代替系统的未知变量,进而提出了非均匀采样数据系统的辅助模型随机梯度辨识算法.为了提高算法收敛速度和改善参数估计精度,在算法中引入遗忘因子,给出了相应的辅助模型带遗忘因子随机梯度算法.仿真结果表明,引入遗忘因子后,算法的收敛速度加快,参数估计精度提高.     

非均匀周期采样系统的递阶最小二乘辨识方法

针对非均匀周期采样系统,通过状态空间模型离散化方法得到其输入输出表达形式.鉴于参数化后得到的辨识模型同时包含1个参数向量和1个参数矩阵,利用递阶辨识原理,将辨识模型分解为分别含有参数向量和参数矩阵的2个虚拟子系统;考虑到系统的因果约束问题,将包含参数矩阵的子系统分解为子子系统进行辨识,从而提出这类非均匀采样系统的递阶最小二乘辨识方法.仿真例子表明该算法是有效的.     

小型无人直升机纵横角动态耦合辨识建模

针对小型无人直升机耦合建模问题提出了一种频域解耦辨识建模方法,该方法通过处理针对耦合辨识的实验数据得到指定频域范围内被辨识耦合的频域特性,对频域特性进行拟合从而获得耦合模型.提出了适用于多输入输出(MIMO)系统的频域特性计算方法,定义了一种复合相干函数并证明其能够用于表达在耦合通道辨识中输入输出的相关性.基于该方法,对一种小型无人直升机在悬停状态的纵横角动态耦合模型进行了辨识,并将耦合模型加入到直升机仿真模型中考察其对模型预测精度的影响.模型预测输出与实际输出的比较表明,相较于普通模型,考虑了耦合动态的仿真模型能够更为精确地预测实际输出.     

一种基于小波多分辨分析的多率采样系统辨识方法

基于小波多分辨分析理论并结合面向控制的辨识思想, 提出一种多率采样系统分频段加权辨识方法. 首先研究了采样信息的一致性重构问题, 然后给出一种新颖的分频段加权辨识方法. 此方法的最大特点是对噪声不敏感, 易实现对感兴趣频段的精确建模, 便于和控制系统设计相配合. 仿真结果验证了这种方法的可行性.     

基于子空间方法的非均匀周期刷新和采样系统辨识

针对非均匀周期刷新和采样系统的建模问题, 对于含有提升变量的状态空间模型, 提出基于子空间技术的辨识方法. 首先, 通过系统的采样数据建立由Hankel 矩阵组成的扩展状态空间方程; 然后, 利用斜交投影的原理、方法和奇异值分解, 通过子空间辨识算法确定增广观测矩阵和状态向量, 通过最小二乘方法确定模型的参数矩阵; 最后, 通过仿真实例表明了所提出算法的有效性.

     

基于PEM的三自由度直升机模型辨识

针对三自由度(3-DOF)直升机平台的特点,提出了一种基于预测误差法(PEM)的模型频域辨识方法,建立了机理模型,运用扫频技术得到巡航飞行状态直升机3个通道的输入-输出数据;分析了偏相干函数和复合窗函数,通过PEM进行了模型的频域辨识,得到了状态空间方程的待辨识参数和直升机的参数化模型.通过时域飞行和模型预测响应的对比,验证了该模型的准确性和该辨识方法的有效性.     

动力调谐陀螺仪系统辨识方法

针对动力调谐陀螺仪(DTG）系统辨识中,传统辨识方法(最小二乘类辨识法和频域辨识法）辨识拟合度不高的问题,提出去离群点频域辨识法。该方法结合DTG模型结构特征和固有有色噪声特点,将去离群点思想应用于DTG模型的频域辨识。实验结果表明,去离群点频域辨识法的辨识效果优于最小二乘类辨识法和传统频域辨识法,辨识拟合度在90%以上,并且辨识结果重复性好,辨识算法稳定。在DTG系统辨识中,去离群点频域辨识法能够提高辨识拟合度。     

动力系统可辨识性研究综述

“可辨识性”是模型能否由观测数据唯一确定的性质,在经济学、生物学、化学、控制科学等多学科中被系统性地研究.近二十年来,随着动力系统复杂性急剧增加,将系统建模为动力学网络的研究愈加普遍,网络可辨识性也越来越受到学界关注.“可辨识性”不仅是系统辨识的理论保证,而且可以作为建模中实验设计、数据采集等的理论指导.本文综述动力系统的可辨识性问题,首先,给出了可辨识性问题的描述及相关定义.根据模型不同类别,本文对线性时不变系统与系统矩阵相关的可辨识性经典结论进行了阐述,并对非线性时不变系统的输入输出方法、输出相等方法等可辨识性主要研究方法进行阐述.针对激励矩阵和观测矩阵的4种不同情况,本文对动力学网络模型的可辨识性问题和代表性成果进行综述.最后,本文讨论了其仍需解决的问题和未来的研究方向.     

多采样率数字控制系统综述

本文给出了多采样率数字控制系统的发展和目前研究情况的综述．根据系统中各采样周期之间的关系，多采样率控制系统可以分为输入多采样率控制系统输出多采样率控制系统和广义多采样率控制系统等．它们又分别对应于采用广义保持器、广义采样器和周期时变控制器的数字控制系统．文中对这三类系统的特点和近期研究成果进行了系统的介绍与讨论．由于“因果条件”， 多采样率控制器具有结构约束．本文综述了几类满足这一结构约束的多采样率控制器的设计方法和各类多采样率控制系统．     

Nonlinear predictive control of irregularly sampled multirate systems using blackbox observers

This work aims the development of an inferential nonlinear model predictive control (NMPC) scheme based on a nonlinear fast rate model that is identified from irregularly sampled multirate data, which is corrupted with unmeasured disturbances and measurement noise. The model identification is carried out in two steps. In the first step, a MISO fast rate nonlinear output error (NOE) model is identified from the irregularly sampled output data. In the second step, a time varying nonlinear auto-regressive (NAR) type model is developed using the residuals generated in the first step. The deterministic and stochastic components of the observer are parameterized using generalized ortho-normal basis filters (GOBF). The identified NOE and NAR models are combined to form MISO state observers. We then proceed to use these identified observers to formulate a nonlinear MPC strategy for controlling irregularly sampled multirate systems. The identified observers are used to generate inter-sample estimates of the irregularly sampled outputs and for performing future trajectory predictions. The efficacy of the proposed modeling and control scheme is demonstrated using simulations on a benchmark continuous fermentation process. This process exhibits input multiplicity and change in the sign of steady state gain in the operating region. The validity of the proposed modeling and control scheme is also established by conducting identification and control experiments on a laboratory scale heater-mixer setup. The proposed NMPC gives satisfactory regulatory as well as servo performance over a wide operating range in the irregularly sampled multirate scenario.     

Modeling and Identification of Multirate Systems

Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950s, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.     

Generalized predictive control for non-uniformly sampled systems

In this paper, we study digital control systems with non-uniform updating and sampling patterns, which include multirate sampled-data systems as special cases. We derive lifted models in the state-space domain. The main obstacle for generalized predictive control (GPC) design using the lifted models is the so-called causality constraint. Taking into account this design constraint, we propose a new GPC algorithm, which results in optimal causal control laws for the non-uniformly sampled systems. The solution applies immediately to multirate sampled-data systems where rates are integer multiples of some base period.     

Simplification of the characteristic equation of multirate sampled data control systems

A method of simplifying the characteristic equation of two classes of linear time invariant multirate sampled data systems to a form similar to that of the analogous single-rate system is developed. The resulting equations are useful for multirate system design.     

Multirate sampled‐data stabilization for a class of low‐order lower‐triangular nonlinear systems

This paper investigates the problem of sampled‐data controller design for a class of lower‐triangular systems in the p‐normal form (0<p<1). A multirate digital feedback control scheme is proposed to achieve the global strong stabilization of the sampled‐data closed‐loop system under some assumptions. In the design of the controller, the input‐Lyapunov matching strategy and multirate control approach are combined to obtain better stabilizing performance. Unlike the design method based on the approximate discrete‐time model, our controller is obtained from the exact discrete‐time equivalent model, which does not need to be computed completely. The approximate multirate digital controllers are proved to be effective in the practical implementation. It is shown that, compared with the emulated control scheme, our controller may provide faster decrease of Lyapunov function for each subsystem. This will lead to allow large sampling periods. An illustrative example is provided to verify the effectiveness of the proposed control scheme.     

A new optimal multirate control of linear periodic andtime-invariant systems

The optimal multirate design of linear, continuous-time, periodic and time-invariant systems is considered. It is based on solving the continuous linear quadratic regulation (LQR) problem with the control being constrained to a certain piecewise constant feedback. Necessary and sufficient conditions for the asymptotic stability of the resulting closed-loop system are given. An explicit multirate feedback law that requires the solution of an algebraic discrete Riccati equation is presented. Such control is simple and can be easily implemented by digital computers. When applied to linear time-invariant systems, multirate optimal feedback optimal control provides a satisfactory response even if the state is sampled relatively slowly. Compared to the classical single-rate sampled-data feedback in which the state is always sampled at the same rate, the multirate system can provide a better response with a considerable reduction in the optimal cost. In general, the multirate scheme offers more flexibility in choosing the sampling rates     

Multirate versions of sampled-data stabilization of nonlinear systems

The problem of state feedback sampled-data stabilization of nonlinear systems is considered under the “low measurement rate” constraint and in the presence of (not necessarily small) time delay in the measurement channel. A multirate control scheme is proposed that utilizes a numerical integration scheme to approximately predict the current state from the delayed measurements. For both the controller emulation approach and the approach based on approximate discrete-time model of the system, we show that under standard assumptions the closed-loop multirate sampled data system is asymptotically stable in the semiglobal practical sense. An illustrative example of sampled-data control of vertical take off and landing aircraft is discussed.