基于过采样结构的贝叶斯辨识方法

将过采样闭环结构与贝叶斯变分法相结合,推导出基于过采样闭环结构的递推贝叶斯变分法,并且通过分析过采样闭环结构估计模型的渐近方差表达式,得出过采样结构可以利用超出模型频带之外的高频信息减小辨识模型的误差。仿真结果表明:基于过采样结构的贝叶斯变分法在输出噪声仅为白噪声情况下,相较于传统辨识方法具有更高的辨识精度。当输出噪声受到尖峰噪声或脉冲噪声污染时,笔者方法能够利用外加噪声中含有的高频信息提高辨识精度。     

多变量时滞系统的闭环渐进辨识研究与应用

多变量时滞系统由于滞后以及相互之间的耦合而难以得到理想的辨识效果,因此基于渐进理论,提出一种多变量的闭环辨识方法.采用阶跃作为测试信号,利用两阶段闭环辨识算法进行辨识.首先采用递推ARX算法实现高阶ARX模型对真实模型的逼近,消除耦合,然后利用二阶泰勒近似实现对滞后的辨识,最后采用渐进准则(ASYC)对模型进行降阶,利用输出误差(OE)准则对降阶后的模型进行参数估计.该方法在实际项目的应用中得到了较好的效果,应用前景广阔.     

基于改进粒子群优化的连续系统闭环辨识新方法

将粒子群优化算法(PSO)的全局辨识能力以及无约束多维寻优法--Rosenbrock的局部搜索能力相结合提出一种全新的参数辨识方法.该方法不需要控制器的先验知识,对测试信号无任何要求.仿真实验表明,与PSO-SQP算法及两阶段闭环辨识法相比,该方法对连续系统在响应不充分的情况下有良好的辨识效果.     

一种基于PSO算法的闭环辨识方法

闭环模型辨识一直是工业先进控制领域中的一个主要课题。而现在基于粒子群优化算法PSO的辨识,大多都是连续开环系统的辨识。离散闭环模型辨识在计算机控制、运算量等方面比连续开环系统的辨识有较大的优势。文中讨论了PSO的时变惯性权重算法与参数初值的设置和选择方法。通过仿真实验表明,PSO与最小二乘递推算法相比,在有效性和一致性方面,有着明显的优势。PSO算法是一种有效地解决优化问题的群集智能算法,它的突出特点是算法中需要选择的参数少,程序实现简单,并在种群数量、寻优速度等方面较其他进化算法具有一定的优势。该方法在实际项目的应用中取得了较好的效果,应用前景广阔。     

汽化炉动态特性的开环与闭环辨识

本文介绍了在上海吴泾化工厂甲醇车间的汽化炉上进行了开环与闭环形式的离线辨识。结果表明,由于过程中存在差频率较高,幅值很大的漂移,开环辨识即使用高通数字滤波器也不能克服漂移的影响,未能得到具有足够精度的过程模型。而闭环辨识由于反馈控制回路克服了漂移对过程输出的影响,因而得到了具有相当精度的过程模型,而且确保了在辨识实验期间生产过程的安全。     

回旋窑烘干过程的自校正适应式控制

在线辨识是自适应控制的核心。设计步骤为在线模型参数估计和参数自整定。本文采用IVLS辨识算法和Dahlin控制算法。与开环辨识相比,IVLS法的闭环辨识收敛更快,但对激发函数的幅值和噪音更为敏感。     

基于幅值最优化的二自由度PID控制器参数自整定研究

通过两次继电反馈，测得被控对象两个关键信息点的频率特性，从而辨识得到二阶纯滞后模型；然后引入一种二自由度PID控制器结构，根据幅值最优化原理整定PID控制器的参数。仿真实验表明该整定方法使闭环系统具有很好的稳定性和鲁棒性。     

自校正内模控制器

提出了设计自校正内模控制的新方法。该方法把输出预测误差划分为有规律和无规律误差两部分。通过在线辨识建立有规律误差的预报模型，由此模型对误差值进行自适应预报，达到对内模控制器的在线校正，而对无规律的误差只作闭环反馈修正，最后达到消除系统的稳态余差，本方法比通常自校正内模控制算法的运算量大大减少，而在对象参数在一定范围内漂移时，同样能取到自校正效果。     

单螺杆挤出机压力-转速闭环控制系统传递函数研究

通过机理分析法求取了单螺杆挤出机的传递函数——机头压力与螺杆转速之间的动态关系，并通过系统辨识法对其结果进行了验证。结果表明单螺杆挤出机的传递函数是一个比例环节。利用此结果和闭环系统中其他环节的传递函数，给出了挤出机压力-转速闭环控制系统的数学模型。     

过程辨识在化工中的应用

过程辨识方法按其获得的模型分类,通常可分成非参量模型和参量模型两种。用阶跃响应法或频率响应法辨识过程,得到的是非参量模型,模型的精度较低,一般用于控制系统调节器参数整定。用参数估计方法辨识过程,得到的是线性离散的参量模型,主要用于设计数字控制算法或自适应数字控制。 1981年初我院化工自动化教研组曾经进行过实验室规模热交换器的开环和闭环辨识实验,得到了良好的结果。本文将以此为例     

IMC-PID控制器在炼油厂减压蒸馏装置的应用研究

采用闭环系统辨识算法与鲁棒IMC-PID控制器结合设计新的软件包,将该软件包用于炼油厂减压蒸馏装置的操作控制,生产运行表明：该软件包在炼油厂减压蒸馏装置中的应用降低了减压炉出口温度的波动幅度,提高了产品质量及收率,降低了生产每吨成品油的能耗,给炼油企业带来了可观的经济效益。     

Control relevant on‐line model validation criterion based on robust stability conditions

This paper is concerned with detection and diagnosis of modelling error under closed‐loop conditions. The effect of modelling error on process output error (which is the error between the process output and the simulated output) is first analysed. Then robust stability conditions for on‐line model validation are applied. The main premise is that whenever the closed‐loop system violates the robust stability condition, it is a sign of significant process change and a signal that the control system may become potentially unstable. We relate the process output error with robust stability conditions and introduce three propositions for on‐line model validation. Any process change (or modelling error) that makes the system violate the condition specified by the robust stability theorem can be detected. Simulation examples are presented to demonstrate the applicability of the propositions. An index is also proposed to quantify modelling error in frequency domain. Simulation examples and an experimental case study are presented to demonstrate the use of the new index.     

Closed‐loop Fault Detection Using the Local Approach

Fault detection and isolation (FDI) has become a crucial issue for industrial process monitoring in order to increase availability, reliability and production safety. Model‐based FDI methods rely on a mathematical model and input‐output data of a process to perform detection. The local approach is a new model‐based FDI method that aims to detect slight changes of a system's parametric properties. Closed‐loop detection is an important issue for the local approach since all control systems work under closed‐loop conditions. A new algorithm was proposed to revise the original detection algorithm in order to make it work for closed‐loop data. Simulation results show that the proposed method can detect the changes of parameters of a system that can affect closed‐loop performance.     

A delay‐dependent robust fuzzy MPC approach for nonlinear CSTR

Based on Takagi–Sugeno (T–S) fuzzy models, a robust fuzzy model predictive control (MPC) algorithm is presented for a class of nonlinear time‐delay systems with input constraints. Delay‐dependent sufficient conditions for the robust stability of the closed‐loop system are derived, and the condition for the existence of the fuzzy model predictive controller is formulated in terms of nonlinear matrix inequality via the parallel distributed compensation (PDC) approach. By using a novel matrix transform technique, a receding optimization problem with linear matrix inequality (LMIs) constraints is constructed to design the desired controllers with an on‐line optimal receding horizon guaranteed cost. Finally, an example of continuous stirred tank reactors (CSTR) is given to demonstrate the effectiveness of the proposed results.     

异步电机单神经元自适应智能控制系统

针对传统抽油电机控制器自适应性能较弱的特点,提出一种单神经元模型参考自适应控制方法,此法在模型参考自适应控制基础上,采用单神经元代替复杂神经网络,选择线性函数作为参考模型,并将速度变化考虑进误差函数,采取磁链开环、转速闭环的控制结构建立控制系统,建立基于此控制方法的异步电机控制系统仿真模型,并基于DSP构建实验控制系统,仿真及实验结果表明所述控制器输出平稳,具有参数及负载时变的自适应能力,应用前景广泛。     

Integration of Design and Control: A Robust Control Approach Using MPC

This paper presents a new method to integrate process control with process design. The process design is based on steady‐state costs, .i.e., capital and operating costs. Control is incorporated into the design in terms of a variability cost. This term is calculated based on the non‐linear process model, represented here as a nominal linear model supplemented with model parameter uncertainty. Robust control tools are then used within the approach to assess closed‐loop robust stability and to calculate closed‐loop variability. The integrated method results in a non‐linear constrained optimization problem with an objective function that consists of the sum of the steady costs and the variability cost. Optimization using the traditional sequential approach and the new integrated method was applied to design a multi‐component distillation column using a Model Predictive Control (MPC) algorithm. The optimization results show that the integrated method can lead to significant cost savings when compared to the traditional sequential approach. In addition, an RGA analysis was performed to study the effects of process interactions on the optimization results.     

Delay‐range‐dependent guaranteed cost control for batch processes with state delay

A guaranteed cost control scheme is proposed for batch processes described by a two‐dimensional (2‐D) system with uncertainties and interval time‐varying delay. First, a 2‐D controller, which includes a robust feedback control to ensure performances over time and an iterative learning control to improve the tracking performance from cycle to cycle, is formulated. The guaranteed cost law concept of the proposed 2‐D controller is then introduced. Subsequently, by introducing the Lyapunov–Krasovskii function and adding a differential inequality to the Lyapunov function for the 2‐D system, sufficient conditions for the existence of the robust guaranteed cost controller are derived in terms of matrix inequalities. A design procedure for the controller is also presented. Furthermore, a convex optimization problem with linear matrix inequality (LMI) constraints is formulated to design the optimal guaranteed cost controller that minimizes the upper bound of the closed‐loop system cost. The proposed control law can stabilize the closed‐loop system as well as guarantee H_∞ performance level and a cost function with upper bounds for all admissible uncertainties. The results can be easily extended to the constant delay case. Finally, an illustrative example is given to demonstrate the effectiveness and advantages of the proposed 2‐D design approach. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2033–2045, 2013     

ROBUST CLOSED LOOP LOG MODULUS DESIGN OF SISO SMITH PREDICTORS FOR A SPECIAL CLASS OF PROCESSES

Linear open loop stable or prestabilized SISO processes with uncertain or changing time delays are controlled via a Smith Predictor. The processes under consideration have no other nonminimum phase characteristics than the time delay. Further, the process models have an inherent integration constant of zero or one. A controller design procedure is presented which guarantees a robust closed loop system, where the major tuning parameter of the controller is a nonlinear function of the maximum closed loop Log Modulus and the model-plant mismatch.     

Application of a capacitive transducer for online part weight prediction and fault detection in injection molding

This article extends the applications of in‐mold capacitive transducer to packing stage for on‐line part weight prediction and check‐ring failure detection for injection molding. Experiments under various conditions show that the proposed on‐line part weight prediction is robust and effective, which may pave the way for on‐line closed‐loop part weight control. POLYM. ENG. SCI., 47:347–353, 2007. © 2007 Society of Plastics Engineers.