连续时间模型的闭环子空间辨识

本文对直接使用采样数据进行连续系统的闭环子空间辨识问题进行了研究．将线性滤波方法与基于主 元分析的子空间辨识相结合,利用参考输入或者外部激励信号的高阶滤波变换的正交投影变量作为辅助变量,提出 了一种新的连续时间系统闭环子空间辨识算法．数值仿真表明了与其他算法相比,本文提出的算法具有很好的辨识 效果．     

鲁棒辨识问题评述

本文综述了一类新的辨识问题－鲁棒辨识问题的研究进展，首先介绍了鲁棒辨识问题产生的背景，然后对各种不同的辨识方法进行了评述，并指出了各自的特点，最后总结了今后的研究方向。     

闭环子空间辨识方法的分析与比较

在实际应用中,辨识方法的辨识精度和辨识效率一直是人们关注的指标,也是人们选择辨识方法的主要依据。针对多种闭环子空间辨识方法的辨识精度和辨识效率问题的研究,首先归纳和总结了基于正交分解和基于正交投影闭环子空间辨识方法的理论和实现;然后扩展提出了基于正交分解的闭环子空间辨识方法 ORT_POMOESP、ORT_N4SID和基于正交投影的闭环子空间辨识方法 CSOPIM_W2;最后考虑系统输入输出测量噪声,针对过程噪声为白噪声和有色噪声两种情况下,通过仿真算例以数值分析的形式,对比研究了多种闭环子空间辨识方法的辨识精度和辨识效率。该研究不仅对子空间辨识方法应用于实际工业过程的建模具有实际的参考价值,而且对实际工程应用中闭环子空间辨识算法的选用具有一定的指导意义。     

一种PI闭环辨识新方法

提出了一种闭环辨识的新方法。通过时域法得到的闭环传递函数与假定过程模型所得的闭环传递函数在相角为 90°和 1 80°两个点上的模和相角的关系 ,从而辨识出假定模型中的未知参数 ,仿真实验的结果表明 ,该方法是可行的 ,并且有较好的精度。     

实现闭环系统参数─致估计的直接辨识方法

为了实现闭环系统参数的一致估计，提出了一种一致辨识闭环系统模型参数的偏差补偿最小二乘法。这种方法不需要事先假设系统模型阶次和通道时延满足一定的不等式，允许前向通道和反馈通道之一中的噪声为有色噪声，并且在其辨识过程中不需要对有色噪声建模。     

基于新息估计和正交投影的闭环子空间模型辨识

针对闭环控制系统提出一种基于新息估计和正交投影的闭环子空间模型辨识方法.首先采用最小二乘法对VARX模型（Vector autoregressive with exogenous inputs model）进行计算得到新息估计值,然后通过将由观测输入输出数据构造的Hankel矩阵正交投影到新息数据的正交补空间以消除噪声影响,从而在无噪声的输入输出数据奇偶空间中提取得到扩展可观测矩阵和下三角形Toeplitz矩阵.最后采用平移变换法得到系统矩阵.对该算法严格分析和证明了实现一致估计的条件.通过仿真实例验证了本文方法的有效性和优越性.     

Identification and robust control for systems with ellipsoidal parametric uncertainty by convex optimization

In this paper, sufficient conditions for robust output feedback controller design for systems with ellipsoidal parametric uncertainty are given in terms of solutions to a set of linear matrix inequalities (LMIs) Performance specifications are in terms of combined pole placement with sensitivity function shaping in the H₂ or H_∞ norm. Furthermore, an optimal input design technique for parameter estimation that is integrated into the robust control design is employed in this paper. This means that performance specifications on the closed‐loop transfer functions are translated into the requirements on the input signal spectrum. The simulation results show the effectiveness of the proposed method. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

For model-based control design, closed-loop identification gives better performance

We compare open loop versus closed loop identification when the identified model is used for control design, and when the system itself belongs to the model class, so that only variance errors are relevant. Our measure of controller performance (which is used as our design criterion for identification) is the variance of the error between the output of the ideal closed loop system (with the ideal controller) and that of the actual closed loop system (with the controller computed from the identified model). Under those conditions, we show that, when the controller is a smooth function of the input-output dynamics and the disturbance spectrum, the best controller performance is achieved by performing the identification in closed loop with an operating controller that we characterize. For minimum variance and model reference control design criteria, we show that this ‘optimal operating controller for identification’ is the ideal controller. This then leads to a suboptimal but feasible iterative scheme.     

基于模糊自适应PID的光伏并网双闭环控制

阐述了光伏并网双闭环控制的基本原理,在此基础上研究了基于模糊自适应PID控制的双闭环控制系统,最后通过Matlab仿真验证了模糊自适应PID控制系统的可行性和优越性。     

Model validation for control and controller validation in a prediction error identification framework—Part II: illustrations

In this paper, we illustrate our new results on model validation for control and controller validation in a prediction error identification framework, developed in a companion paper (Gevers et al., Automatica (2003) 39(3) pii: S005-1098(02)00234-0), through two realistic simulation examples, covering widely different control design applications. The first is the control of a flexible mechanical system (the Landau benchmark example) with a tracking objective, the second is the control of a ferrosilicon production process with a disturbance rejection objective.     

Integrated identification and robust control

A framework for integrated identification and control is presented. As part of this framework, frequency domain uncertainty bounds are derived for robust stability tests, a robust stability test for elliptical bounds is developed for SISO systems, a methodology for estimating controller performance is derived, and an optimal experiment design methodology for control-relevant identification is outlined. An example is presented to illustrate how the tools of the framework fit together.     

A two‐degree‐of‐freedom ℋ︁∞ control design method for robust model matching

We propose an ??_∞ controller design method which achieves a closed‐loop transfer function equal or otherwise sensibly close to a desired transfer function, viz. a model reference design. The proposed controller design method inherits the model reference feature of the internal model control design method and incorporates the weighting scheme of the ??_∞ loop‐shaping. It utilizes Youla–Kucera parameterization in a two‐degree‐of‐freedom scheme to achieve robust model reference and high performance design while ensuring a sensible robust stability margin, and can be readily applied to the generic class of LTI systems (SISO, MIMO, stable, unstable). Copyright © 2006 John Wiley & Sons, Ltd.     

A learning theory approach to system identification and stochastic adaptive control

In this paper, we present an approach to system identification based on viewing identification as a problem in statistical learning theory. Apparently, this approach was first mooted in [E. Weyer, R.C. Williamson, I. Mareels, Sample complexity of least squares identification of FIR models, in: Proceedings of the 13th World Congress of IFAC, San Francisco, CA, July 1996, pp. 239–244]. The main motivation for initiating such a program is that traditionally system identification theory provide asymptotic results. In contrast, statistical learning theory is devoted to the derivation of finite-time estimates. If system identification is to be combined with robust control theory to develop a sound theory of indirect adaptive control, it is essential to have finite-time estimates of the sort provided by statistical learning theory. As an illustration of the approach, a result is derived showing that in the case of systems with fading memory, it is possible to combine standard results in statistical learning theory (suitably modified to the present situation) with some fading memory arguments to obtain finite-time estimates of the desired kind. It is also shown that the time series generated by a large class of BIBO stable nonlinear systems has a property known as β-mixing. As a result, earlier results of [E. Weyer, Finite sample properties of system identification of ARX models under mixing conditions, Automatica, 36 (9) (2000) 1291–1299] can be applied to many more situations than shown in that paper.     

Closed-loop identification: a two step approach

The accuracy aspects of identification (with respect to both variance and bias of estimates) and the role of filtering in closed-loop identification is discussed in this paper. It is shown that the key difference between closed-loop and open-loop identification is the existence of the sensitivity function. A closed-loop identification algorithm which asymptotically yields the same expressions as open-loop identification, in both variance and bias errors, is proposed. The proposed algorithm is evaluated by simulated examples as well as experiments performed on a computer-interfaced pilot-scale process.     

Identification and control of nonlinear systems by a time-delay recurrent neural network

In this paper, we first present a novel time-delay recurrent neural network (TDRNN) model by introducing the time-delay and recurrent mechanism. The proposed TDRNN model has special advantages such as simple structure, deeper depth and higher resolution ratio in memory. Thereafter, we develop the dynamic recurrent back-propagation algorithm for the TDRNN. To guarantee the fast convergence, the optimal adaptive learning rates are also derived in the sense of discrete-type Lyapunov stability. More specifically, a TDRNN identifier and a TDRNN controller are constructed to perform the identification and control of the nonlinear systems. Numerical experiments show that the TDRNN model has good effectiveness in the identification and control for dynamic systems.     

Closed-loop magnetic control of a spin-stabilized satellite

Autonomous closed-loop magnetic control is achievable for a spinning satellite in a near magnetically polar orbit for maintaining a programmed spin rate and a spin pointing-axis normal to the orbit plane. The system requires a spin-pointing coil to generate magnetic moments parallel to the spin axis, a spin rate coil with an axis normal to the spin vector, an earth sensor, a magnetometer measuring the earth field components normal to the spin, and electronic logic and circuitry to perform timing measurements between the earth sensor pulses and detect errors in spin rate and pointing elevation angles between the spin axis and the horizontal (roll). Programmed altitude corrections to these timing measurements are required for non-circular orbits. In addition, the electronics make sequenced samples of the magnetometer signals, perform the control law logic, and command the electrical connections to energize the magnetic coils. The spin control generates magnetic torque when the field is detected in the appropriate direction to provide spin torque in the direction to correct spin rate.     

Decentralized Closed-loop Identification and Controller Design for a Kind of Cascade Systems

Decentralized closed-loop identification and controller design for a kind of cascade pro- cesses composed of several sub-processes are studied.There are only input couplings between adjacent sub-processes.First,the cascade process is divided into several two-input two-output systems and coupling models of adjacent sub-processes are obtained via decentralized identification.TITO sys- tem is decoupled equivalently into four independent single open-loop processes with the same input signal.Then,a distributed model predictive control algorithm is proposed based on the coupling models of adjacent sub-processes.     

一类串联系统的分散闭环辨识与控制器设计

Decentralized closed-loop identification and controller design for a kind of cascade processes composed of several sub-processes are studied. There are only input couplings between adjacent sub-processes. First, the cascade process is divided into several two-input two-output systems and coupling models of adjacent sub-processes are obtained via decentralized identification. TITO sys- tem is decoupled equivalently into four independent single open-loop processes with the same input signal. Then, a distributed model predictive control algorithm is proposed based on the coupling models of adjacent sub-processes.     

具有Markov跳变参数的闭环供应链系统切换控制

研究具有 Markov 跳变参数的闭环供应链(Closed-loop supply chain, CLSC)切换系 统建模以及具有抑制牛鞭效应的H∞控制问题. 针对再制造过程中的不确定性问题, 在考虑库存衰减因素的条件下, 根据库存水平的不同状态将系统建模为切换系统, 子系统间的切换服从 于一个Markov过程. 基于输入滞后的控制策略, 应用Markov切换思想对 系统进行控制器设计与性能分析, 在保证闭环供应链系统稳定的情形 下有效抑制牛鞭效应. 仿真例子说明所得结果的有效性.