An integrated advanced process control framework using run-to-run control,virtual metrology and fault detection

This paper develops a new advanced process control (APC) system for the multiple-input multiple-output (MIMO) semiconductor processes using the partial least squares (PLS) technique to provide the run-to-run control with the virtual metrology data, via the gradual mode or the rapid mode depending on the current system status, in order to deal with metrology delays and compensate for different types of system disturbances. First, we present a controller called the PLS-MIMO double exponentially weighted moving average (PLS-MIMO DEWMA) controller. It employs the PLS method as the model building/estimation technique to help the DEWMA controller generate more consistent and robust control outputs than purely using the conventional DEWMA controller. To cope with metrology delays, the proposed APC system uses the pre-processing metrology data to build up the virtual metrology (VM) system that can provide the estimated process outputs for the PLS-MIMO DEWMA controller. Lastly, the Fault Detection (FD) system is added based upon the principal components of the PLS modeling outcomes, which supplies the process status for the VM mechanism and the PLS-MIMO DEWMA controller as to how the process faults are responded. Two scenarios of the simulation study are conducted to illustrate the APC system proposed in this paper.     

A Setup for Active Fault Diagnosis

A setup for active fault diagnosis (AFD) of parametric faults in dynamic systems is formulated in this note. It is shown that it is possible to use the same setup for both open loop systems, closed-loop systems based on a nominal feedback controller as well as for closed-loop systems based on a reconfigured feedback controller. This will make the proposed AFD approach very useful in connection with fault tolerant control (FTC). The setup will make it possible to let the fault diagnosis part of the fault tolerant controller remain unchanged after a change in the feedback controller. The setup for AFD is based on the Youla–Jabr–Bongiorno–Kucera (YJBK) parameterization of all stabilizing feedback controllers and the dual YJBK parameterization. It is shown that the AFD is based directly on the dual YJBK transfer function matrix. This matrix will be named the fault signature matrix when it is used in connection with AFD.     

PLS based dEWMA run-to-run controller for MIMO non-squared semiconductor processes

This paper focuses on the modification of the PLS (partial least squares) modeling. The new method allows incorporation of a dEWMA (double exponentially weighted moving average) control algorithm into the standard run-to-run controller design for semiconductor processes. The resulting structure of the PLS model can extract the strongest relationship between the input and the output variables. It is particularly useful for inherent noise suppression. In addition, the resulting non-square MIMO control system can be decomposed into a multi-loop control system by employing pre-compensators and post-compensators of the PLS model, which is constructed from the input and output loading matrices. Subsequently, the conventional dEWMA controller can be separately and directly applied to each SISO control loop. The performance of the proposed method is illustrated through a chemical–mechanical polishing process in the manufacturing of the semiconductor.     

偏最小二乘线性模型及其非线性动态扩展模型综述

偏最小二乘(Partial least square,PLS)是一种基于数据驱动可以处理多个因变量对多个自变量的回归建模方法,因其具有提取质量相关信息的特性,在质量相关复杂工业过程监控中得到广泛的应用,成为近几十年复杂工业过程故障检测和诊断领域的研究热点.对此,介绍线性、非线性、动态PLS模型及其故障检测技术.首先,介绍标准PLS模型,在此基础上对传统PLS模型进行细化分并指出其优缺点,针对标准PLS存在的两个问题以及工业过程数据的两种极端情况,从数据预处理类、多空间类和分块类三方面梳理线性PLS模型的发展和改进历程;其次,将非线性PLS模型扩展方法分为两类,重点介绍核函数非线性PLS模型的研究现状;再次,指出动态扩展方法的两种基本思路,对PLS动态模型进行分类,阐明动态特性的成因,从本质上揭示两种动态扩展方法的原理,按照分类综述动态PLS模型的发展现状;最后,指出该领域亟需解决的问题和未来研究方向.     

Designs of Bisimilar Petri Net Controllers With Fault Tolerance Capabilities

This paper proposes an approach for providing tolerance against faults that may compromise the functionality of a given controller modeled by a Petri net. The method is based on embedding the given Petri net controller into a larger (redundant) Petri net controller that retains the original functionality and properties, and uses additional places, connections, and tokens to impose invariant conditions that allow the systematic detection and identification of faults via linear parity checks. In particular, this paper considers two types of redundant Petri net controllers: 1) nonseparate redundant Petri net controllers have the same functionality as the given Petri net controller and allow for fault detection and identification, but do not necessarily retain the given controller intact; and 2) separate redundant Petri net controllers are a special case of the nonseparate redundant controllers that retain the given Petri net controller intact but enhance it with additional places to enable fault detection and identification. The work in this paper obtains complete characterizations of both types of redundant controllers along with necessary and sufficient conditions for them to be bisimulation equivalent to the given original Petri net controller. In addition, this paper discusses how each type of redundant controllers can be designed to have desirable fault detection and identification capabilities. When the bisimulation equivalence requirement is not directly enforced, nonseparate redundant controllers can potentially have advantages over separate ones (e.g., they can use fewer connections to detect and identify the same number of faults). An example of a Petri net controller for a production cell and its fault tolerance capabilities using separate and nonseparate embeddings is used to illustrate the approach.     

A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control

This paper presents a performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control for large-scale systems. After the fault has been detected and diagnosed, several controller reconfigurations are proposed as candidate corrective actions for fault compensation. The solution of a set of constrained optimization problems with different actuator and setpoint reconfigurations is derived by means of an original approach, exploiting the information on the active constraints in the non-faulty subsystems. Thus, the global optimization problem is split into two optimization subproblems, which enable the online computational burden to be greatly reduced. Subsequently, the performances of different candidate controller reconfigurations are compared, and the better performing one is selected and then implemented to compensate the fault effects. Efficacy of the proposed approach has been shown by applying it to the benzene alkylation process, which is a benchmark process in distributed model predictive control.     

Relationships between Linear Dynamically Varying Systems and Jump Linear Systems

The connection between linear dynamically varying (LDV) systems and jump linear (JL) systems is explored. LDV systems have been used to model the error in nonlinear tracking problems. Some nonlinear systems, for example Axiom A systems, admit Markov partitions and their dynamics can be quantized as Markov chains. In this case the tracking error can be approximated by a JL system. It is shown that (i) JL controllers for arbitrarily fine partitions exist if and only if the LDV controller exists; (ii) the JL controller stabilizes the nonlinear dynamical system; (iii) JL controllers provide approximations of the LDV controller. Finally, it is shown that this process is robust against such errors in the probability structure as the inaccurate assumption that an easily constructed partition is Markov. Date received: August 20, 2001. Date revised: June 5, 2003. This research was supported by National Science Foundation Grant ECS-98-02594.     

基于多线程的顾客满意度PLS路径模型算法

在计算顾客满意度模型时, 普遍使用结构方程模型来求解复杂的多维度关系. 而针对结构方程模型的计算, 偏最小二乘(PLS)有效地解决了模型分析过程中大量潜变量无法直接采样获得的问题, 建立了显变量与潜变量之间的关系, 增强了模型的直观性和通用性. 为了提升偏最小二乘算法的效率, 在针对结构方程模型的计算中, 利用多线程将模型计算并行化, 以提升计算效率. 探讨了基于Java多线程的顾客满意度PLS路径模型的优化算法, 并在多核计算机上实验证明, 多线程化的PLS路径模型能够显著提升计算速度.     

A multivariate statistical controller for on-line quality improvement

Producing good quality products is an important process control objective. However, achieving this objective can be very difficult in a continuous process, especially when quality measurements are not available on-line or they have long time delays. In this paper, a control approach using multivariate statistical models is presented to achieve this objective. The goal of the control approach is to decrease variations in product quality without real time quality measurements. A PCA model which incorporates time lagged variables is used, and the control objective is expressed in the score space of this PCA model. A controller is designed in the model predictive control (MPC) framework, and it is used to control the equivalent score space representation of the process. The score predictive model for the MPC algorithm is built using partial least squares (PLS). The proposed controller can be developed from and implemented on top of existing PID control systems, and it is demonstrated in two case studies, which involve a binary distillation column and the Tennessee Eastman process.     

Nonlinear inferential control of an autonomous periodic fixed-bed reactor

This paper deals with the control of an autonomous periodic fixed-bed reactor, which is called circulation loop reactor (CLR). This process can be described by a distributed parameter system with sustained oscillations. The state variables at some positions of such a process, e.g. the conversion at the reactor exit, may be required to vary in a smooth fashion, which in general is a difficult task due to the process's inherent complex behavior. Here, a straightforward control strategy of the CLR based on direct exit concentration regulation is presented. A relay feedback controller is found to be effective for achieving overall conversion while using relatively small external heating. In order to implement the control scheme without on-line concentration measurement, a nonlinear inferential control concept is adopted. A newly-developed nonlinear distributed parameter observer is used to infer the exit concentration from the temperature measurements at only a few positions. Moreover, an approach based on integrating a disturbance estimator is presented for improving the performance of the observer in the case of unmeasured inlet concentration. It is found that there exist simple determinate relations between the inlet variables or the model parameters and the maximum amplitude or the period of the oscillatory temperature at an axial position for the considered cases. By employing the oscillatory feature of the CLR, a method of estimating the inlet concentration is proposed, and a parameter estimation method based on the least square technique is given to update offline model parameters. It is shown that substantial benefits could be realized using the proposed nonlinear inferential control scheme for the CLR because it can provide excellent disturbance rejection capabilities and strong robustness.     

Fuzzy control of an ANFIS model representing a nonlinear liquid-level system

This paper aims to serve two main objectives; one is to demonstrate the modelling capabilities of a neuro-fuzzy approach, namely ANFIS (adaptive-network based fuzzy inference system) to a nonlinear system; and the other is to design a fuzzy controller to control such a system. The nonlinear system, which is a liquid-level system, is represented first by its mathematical model and then by ANFIS architecture. The ANFIS model is formed by means of input–output data set taken from the mathematical model. Then a PID-type fuzzy controller, which linguistically approximates the classical three-term compensation, was designed to control the system represented by both its mathematical and ANFIS models in order to perform an agreement comparison between them. It is shown that the ANFIS architecture can model a nonlinear system very accurately by means of input–output pairs obtained either from the actual system or its mathematical model. It is also shown that such a system can be controlled effectively by a fuzzy controller.     

Theoretical analysis of unconstrained nonlinear model predictive control

Model predictive control (MPC) is a well-established controller design strategy for linear process models. Because many chemical and biological processes exhibit significant nonlinear behaviour, several MPC techniques based on nonlinear process models have recently been proposed. The most significant difference between these techniques is the computational approach used to solve the nonlinear model predictive control (NMPC) optimization problem. Consequently, analysis of NMPC techniques is often connected to the computational approach employed. In this paper, a theoretical analysis of unconstrained NMPC is presented that is independent of the computational approach. A nonlinear discrete-time, state-space model is used to predict the effects of future inputs on future process outputs. It is shown that model inverse, pole-placement, and steady-state controllers can be obtained by suitable selection of the control and prediction horizons. Moreover, the NMPC optimization problem can be modified to yield nonlinear internal model control (NIMC). The computational requirements of NIMC are considerably less than NMPC, but the NIMC approach is currently restricted to nonlinear models with well-defined and stable inverses. The NIMC controller is shown to provide superior servo and regulatory performance to a linear IMC controller for a continuous stirred tank reactor.     

Model-based, real-time control of electrical power systems

Automated control of a large electrical power distribution network through a single controller can provide advantages in efficiency and reliability as well as reduction in maintenance costs. For control to be most effective, it is necessary that a global view of the entire network be had by the controller, so that it can reason as to the cause of the readings of the various sensing devices located throughout the network, Traditional approaches to power system control have involved a set of local devices (i.e., protective relays) that base their decision on the instantaneous reading of a single sensor. These single-parameter decisions can sometimes be incorrect due to sensor failures. Furthermore, a special type of fault called a soft fault, where a fault impedance limits the current to a value below the relay operating point, are nearly impossible to detect with decisions based on a single local parameter. By reasoning over an entire suit of sensing devices spread throughout the entire network, protection decisions based on a global view can become more reliable as well as comprehensive. Some previous approaches have implemented global control with varying degrees of success through the use of rule-based knowledge-based systems. This paper describes an alternative knowledge-based approach that makes use of so-called models of structure-and-behavior, to which model-based diagnosis is applied. The objective of this approach is to develop a system that can reliably diagnose faults in power distribution networks (especially soft faults), identify sensor failures, and carry out appropriate corrective action automatically. An intelligent power controller (IPC) which has these capabilities is described. This IPC was rigorously tested in an DC electrical power distribution system testbed and found to successfully carry out the required functions. This paper also describes in detail the tests and the conclusions drawn from their results     

改进的遗传算法在汽车故障诊断中的应用

本文提出了一种基于遗传算法的自适应模型来预测发动机部件的特征图。将数值模型和测试数据的主要性能参数的区别函数作为目标函数,并且考虑了元件特性图的耦合因子作为优化参数,自适应模型和测试数据之间的主要性能的参数和过程参数差异范围显示在0.05％内。同时,该部分的总温度和压力控制在1％以内。此外,故障诊断模型是通过小偏差方程方法实现的,其中实现了气路分析和症状测量参数,代表发动机性能参数的变化。它表明了症状参数的选择值对故障诊断误差影响很大,最佳选择值为阈值的1/3。故障诊断模型的症状参数与实际故障之间的变量值的相对误差可以控制在5％以内,因而可以正确评估故障类型,并且在故障诊断模型的所有执行中都不存在误诊。     

Applications of hysteresis switching in parameter adaptive control

The hysteresis switching algorithm of R.H. Middleton et al. (ibid., vol.33, no.1, p.50-8, Jan. 1988) is reexamined in a broader context. To demonstrate its utility, the algorithm is applied to various families of identifier-based parameterized controllers of both the direct and indirect control types. Application to the direct control type results in a model reference adaptive controller capable of stabilizing, without excitation, any SISO process which can be modeled by a minimum phase linear system whose transfer function has relative and McMillan degrees not exceeding prescribing integers m and n, respectively. It is shown that such processes can also be adaptively stabilized with indirect adaptive controllers and hysteresis switching. A simple numerical example involving a non-minimum-phase process model is used to illustrate how hysteresis switching might be applied to implicitly tuned parameterized controllers to realize an adaptive controller with capabilities which might prove very difficult, if not impossible, to achieve without hysteresis switching or some other form of discontinuous control     

Multi-way PLS regression: Monotony convergence of tri-linear PLS2 and optimality of parameters

The tri-linear PLS2 iterative procedure, an algorithm pertaining to the NIPALS framework, is considered. It was previously proposed as a first stage to estimate parameters of the multi-way PLS regression method. It is shown that the tri-linear PLS2 procedure is convergent. The procedure generates a sequence of parameters (scores and loadings), which can be described as increasing or decreasing two specific criteria. Furthermore, a hidden tensor is described allowing tri-linear PLS2 to search its best rank-one approximation. This tensor highlights the link between multi-way PLS regression and the well-known PARAFAC model. The parameters of the multi-way PLS regression method can be computed using three alternative procedures.     

Nonlinear PI control of fed-batch processes for growth rate regulation

This paper deals with the regulation of the biomass specific growth rate, which is an important goal in many fed-batch fermentation processes. The proposed control system is based on the minimal model paradigm, requiring only biomass and volume measurement along with some bounds on the reaction rate. The controller has the structure of a partial state feed-back with adjustable gain. An integral-proportional control algorithm is designed to adjust this gain. It is inspired in concepts of invariant control and system immersion. First, a nonlinear integral action that makes invariant a goal manifold defined by a reference model dynamics is developed. Then, a proportional output error feed-back is incorporated to the control law with the aim of fastening convergence. Stability is investigated in detail using Lyapunov functions. To implement the control law, an estimation of the growth rate is required like any other PI-like controller. Because of its strong convergence properties, a sliding observer that requires the same process information as the controller is used for this task, although conventional continuous observers can alternatively be used provided they are fast enough to preserve stability. Simulation results showing the transient response and robustness features of the controller under nominal and perturbed scenarios are presented.     

Informative PLS score-loading plots for process understanding and monitoring

Principal component regression (PCR) based on principal component analysis (PCA) and partial least squares regression (PLSR) are well known projection methods for analysis of multivariate data. They result in scores and loadings that may be visualized in a score-loading plot (biplot) and used for process monitoring. The difficulty with this is that often more than two principal or PLS components have to be used, resulting in a need to monitor more than one such plot. However, it has recently been shown that for a scalar response variable all PLSR/PCR models can be compressed into equivalent PLSR models with two components only. After a summary of the underlying theory, the present paper shows how such two-component PLS (2PLS) models can be utilized in informative score-loading biplots for process understanding and monitoring. The possible utilization of known projection model monitoring statistics and variable contribution plots is also discussed, and a new method for visualization of contributions directly in the biplot is presented. An industrial data example is included.     

MFA控制器控制大时滞过程的实验研究

以大时滞过程为研究对象,对无模型自适应(MFA,M0del-freeAdaptive)控制器进行了仿真和实验研究,并与传统的PID控制器进行了比较,同时进行了鲁棒稳定性的研究。实验结果表明,无模型自适应控制器具有良好的输出跟踪性能,有很强的自适应能力和鲁棒性,能有效地控制复杂过程,特别在控制大时滞过程方面与传统的PID控制器相比,有着明显的优越性。     

From safety analysis to software requirements

Software for safety critical systems must deal with the hazards identified by safety analysis. This paper investigates, how the results of one safety analysis technique, fault trees, are interpreted as software safety requirements to be used in the program design process. We propose that fault tree analysis and program development use the same system model. This model is formalized in a real-time, interval logic, based on a conventional dynamic systems model with state evolving over time. Fault trees are interpreted as temporal formulas, and it is shown how such formulas can be used for deriving safety requirements for software components