基于输入扩张的闭环系统子空间辨识及其强一致性分析

针对闭环条件下的子空间辨识问题, 结合线性代数和几何学的基本概念, 将输入输出误差序列包含至输入子空间中, 基于输入扩张的状态空间构造方法, 提出一种新的闭环辨识算法;解决开环算法应用于闭环系统辨识时产生有偏估计, 甚至不能正确辨识的问题;实现闭环条件下对系统状态空间矩阵的强一致估计, 并理论证明该辨识算法的强一致性;最后通过仿真实例验证本算法的有效性.     

闭环系统的输出过采样辨识方法及其估计精度分析

传统闭环系统辨识方法的可辨识性受到参考设定信号和控制器结构的限制．提出了一种通过对输出过采样实现线性离散时间闭环系统辨识的方法，输出过采样提供了更多的系统结构信息，在传统辨识方法的可辨识条件不满足的情况下，仍能正确辨识系统参数，针对有色噪声干扰，分析其在不同过采样率下的估计精度，得出最优估计的过采样率计算方法．辨识方法实现简单、运算量小、估计精度高．仿真试验验证了其有效性．     

AUV闭环系统可辨识性分析及算法实现

针对水下机器人闭环系统辨识问题,应用闭环系统辨识理论,本文给出了水下机器人闭环系统可辨识的充分条件.运用增广的Kalman滤波算法对某水下机器人进行闭环辨识.获得了该系统模型.验证了所提出充分条件的正确性及算法的可行性.     

脉冲响应模型的闭环子空间辨识方法

针对实际情况中难以采集控制系统开环数据的问题,对脉冲响应模型的闭环辨识问题进行了研究,提出了一种基于正交分解理论的脉冲响应模型闭环子空间辨识方法。通过使用正交分解得到联合输入输出信号的确定部分（Deterministic Components）,把闭环问题转化成开环问题。利用Toeplitz矩阵下三角结构形式,对由脉冲响应模型系数组成的子空间矩阵分块分解,通过求取子空间矩阵元素的平均值来获取脉冲响应模型参数的估计。通过采用PID控制器的单输入单输出（SISO）数值仿真、多输入多输出（MIMO）数值仿真和Wood-Berry蒸馏过程仿真实验3个仿真实例,对比研究了所提方法与PBSID_OPT、CVA三种方法。仿真结果表明了所提出的方法具有良好的辨识性能。对于实际工业过程的建模问题,该研究所提的闭环子空间辨识方法具有实际的参考价值和一定的指导意义。     

对象模型辨识的闭环扩充频率法

从理论上对求对象模型的闭环扩充频率法进行了分析,给出了对象模型辨识的步骤和参数计算公式。由于文中所述四种模型是工业过程常用的近似模型,调节器可以从P推广到PI和PID。因而,本文提出的方法具有一定的实际意义。通过在IBM-PC机进行的仿真,证明这一方法的可实现性。     

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．     

过程对象模型在线闭环频域辨识

提出一种过程对象在线闭环辨识方法.通过对控制回路中过程时象的输入输出信号进行信号分解和频谱分析,得出过程对象在重要频率段上的频率响应特性,然后采用最小二乘法在幅值和相位两方面去拟合一个二阶加纯滞传递函数,最终获得过程对象模型.辨识所需的数据是通过设定值改变后系统过渡变化的输入输出信号获得的,不需要中断现有正常运行而切换到某种试验状态,控制系统仍然工作在正常运行状态中.仿真实验验证了辨识方法的有效性和和精确性.     

基于PPSO的精馏塔模型参数闭环辨识

针对粒子群优化算法(particle swarm optimization,PSO)在高维空间复杂曲面寻优时易于陷入局部最小值的问题,分组扰动粒子群优化算法(partially-perturbed particle swarm optimization,PPSO)结合问题特征,采用启发式规则,实施参数分组扰动策略,对PSO算法进行改进,从而增大了跳出局部极小的可能性。本文主要研究PPSO在精馏塔模型参数闭环辨识上的应用,分别针对模型参数可辨识性,参数的不同分组,鲁棒性进行分析验证;并在其他精馏塔模型上进行了相应的验证。仿真实验表明,PPSO辨识算法比序列近似法等其它辨识算法具有更高的辨识精度,并且具有较强的鲁棒性;在其他精馏塔模型参数辨识上PPSO算法也同样取得了很好的辨识精度。实验结果证明了PPSO算法在精馏塔模型参数闭环辨识上的可行性和有效性。     

一种闭环条件下的多变量系统辨识方法

为得到带有色噪声的闭环多变量系统参数无偏估计,提出两阶段随机梯度辨识算法,此算法利用中间模型将闭环问题转化成两个开环问题,并基于随机梯度算法和递阶辨识原则,用最速下降法极小化预测误差准则来得到参数估计.算法只需一个测试信号,不需要控制器的先验知识,计算简便,能获得满意的结果,适用于闭环多变量系统的在线辨识.     

实现闭环系统参数一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，允许前向通道的反馈通道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。     

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.     

Closed-loop identification with routine operating data: Effect of time delay and sampling time

In industry, in order to store the reams of data that are collected from all the different flow, level, and temperature sensors, the fast-sampled data is very often downsampled before being stored in a data historian. This downsampled or even compressed data is, then, often used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine operating data. It is shown that if the discrete time delay in a process is sufficiently large or the sampling time is sufficiently small, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine operating data. Using both Monte Carlo simulations and an experimental set-up with a heated tank, the effect of discrete time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model. As well, the models obtained using a PID controller were less sensitive to sampling time than those obtained using a PI controller. However, the PID controller values were more sensitive to the effects of aliasing at larger sampling times.     

Closed-loop persistent identification of linear systems with unmodeled dynamics and stochastic disturbances

The essential issues of time complexity and probing signal selection are studied for persistent identification of linear time-invariant systems in a closed-loop setting. By establishing both upper and lower bounds on identification accuracy as functions of the length of observation, size of unmodeled dynamics, and stochastic disturbances, we demonstrate the inherent impact of unmodeled dynamics on identification accuracy, reduction of time complexity by stochastic averaging on disturbances, and probing capability of full rank periodic signals for closed-loop persistent identification. These findings indicate that the mixed formulation, in which deterministic uncertainty of system dynamics is blended with random disturbances, is beneficial to reduction of identification complexity.     

Closed-loop identification with a quantizer

This paper proposes a new closed-loop identification scheme for a single-input single-output (SISO) control loop based upon a quantizer inserted into the feedback path. The quantizer can be used to generate an equivalent persistently exciting signal to which the well known two-stage method of closed-loop identification can be applied. The paper examines the performance and behaviour of the quantizer-based closed-loop identification and gives suggestions for the choice of quantizer interval. Simulation and experimental examples are used to illustrate the proposed new CLID scheme.     

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。     

Identification and control — Closed-loop issues

An overview is given of some current research activities on the design of high-performance controllers for plants with uncertain dynamics, based on approximate identification and model-based control design. In dealing with the interplay between system identification and robust control design, some recently developed iterative schemes are reviewed and special attention is given to aspects of approximate identification under closed-loop experimental conditions.     

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。     

Realizations of BELS as WIV method in both direct and indirect closed-loop system identification

The bias eliminated least squares (BELS) method, which is known as efficient for unknown parameter estimation of transfer function in the correlated noise case, has been developed and applied effectively to the closed-loop system identification. In this paper, under the general settings, the realizations of the BELS method as a weighted instrumental variables (WIV) method in both direct and indirect closed-loop system identification are established through constructing an appropriate weighting matrix in the WIV method. The constructed structures are similar in both cases, which reveals that all the proof procedures of the two realizations are the same. Thus, the unified realizations of the BELS as the WIV method for the closed-loop system identification can be built. A simulation example is given to validate our theoretical analysis. Supported by the National Natural Science Foundation of China for Distinguished Young Scholars (Grant No. 60625104), the Ministerial Foundation of China (Grant No. A2220060039), and the Fundamental Research Foundation of BIT (Grant No. 1010050320810)