A comparison of adaptive control strategies for hot steel strip rolling mills

Many modeling situations occur in which the plant has uncertain dynamics, nonlinearities, time varying characteristics and noise corrupted input and output measurements. These processes generally require a human operator whose function is to provide intelligent modeling and control. This exact situation occurs in the modeling and control of roll force in a hot steel rolling mill. It is the purpose of this paper to investigate and compare various adaptive control strategies for this problem.The first strategy uses a parameter identification technique to track the parameters in the roll force setup model from one steel run to the next. The next algorithm provides feedback control from run to run by an adaptive controller which uses a linear reinforcement learning scheme to adjust its parameters. The third method accounts for the above complexities by approaching the problem from a behavioral and structural point of view. The behavior of the model is assessed through a performance evaluator and the model is modified structurally and parametrically to improve the performance of the system as the process evolves. The derivation is based on correlation techniques and linear reinforcement learning theory, the latter of which provides memory and intelligence to the algorithm to model the decision process of the human operator. The results of this work serve to reinforce the opinion that the nonlinear mathematical structure of the model should be able to change from one steel run to the next in order to compensate for changes in mill characteristics and in the mill environment. Modeling results are presented from actual mill data and comparisons are made with time invariant models. In addition, the algorithms are general enough so that they may be easily applied to other processes that seem to defy traditional modeling techniques. They are not case dependent.     

Modelling and prediction of copper concentration of a grinding process

The mathematical model of a grinding process in copper concentrator is presented. With the aid of a dynamic model and a Kalman filter the copper concentration of the output of this process is predicted. The process includes ball mill, autogenous mill and separator. The autogenous mill is working as a secondary grinding mill for the underflow coming from the classifier.To obtain the mathematical model of grinding process several practical tests including screen analysis, puls and impulse tests, were performed. It has been shown that the dynamics of one ball mill can be described with two perfect mixers and plug flow in series and the autogenous mill with two perfect mixers in series. A linear matrix vector model has been used in a Kalman filter for estimating the copper concentration in the input flow of the flotation process.     

Improved identification and autotuning of PI controllers for MIMO processes by relay techniques

A technique for on-line identification and tuning is proposed to be used in the framework of a MIMO autotuning procedure. The proposed technique does not suffer from the risks of instability and the lack in performance of common tuning techniques in MIMO autotuning. Identification is accomplished through an extension of the well known ATV autotune identification method and requires only few additional tests in order to obtain some more knowledge about the process. The resulting model, which describes with good precision the process in a region of frequencies around the critical point, is then used for tuning: the integral time is found as a function of the model time constants and delay, while the gain is computed in order to give a desired value of the closed-loop resonance peak. Examples of application show that advantages over other proposed techniques can be retained for processes having different dynamic characteristics.     

生物特征识别技术研究进展

生物特征身份鉴别方法是根据人体各器官或个人行为之间的差异来鉴别个人身份。随着计算机技术的迅速发展,生物特征鉴别技术将在军事和人们的日常生活等各个方面得到广泛的应用。文章介绍了生物特征的概念及基于生物特征识别的身份鉴别技术,对不同的识别方法的原理、特征做了较详细的分析与评价。对生物特征身份鉴别技术的应用前景和发展方向也做了分析。     

炉卷轧机层流冷却系统的控制策略及精冷段系统辨识

某厂炉卷轧机在冷却控制方面,采用冷却前预设定计算：主冷前馈控制,精冷反馈控制和自适应控制策略的层流冷却系统。文中分析了各个部分的功能,并且对该系统在实际生产中的应用进行了分析评价,提出了系统中问题的改进方向,对精冷过程进行了系统辨识,并做了仿真研究。     

Direct and two-step methods for closed-loop identification: a comparison of asymptotic and finite data set performance

The asymptotic and finite data behavior of some closed-loop identification methods are investigated. It is shown that, when the output power is limited, closed-loop identification can generally identify models with smaller variance than open-loop identification. Several variations on some two-step identification methods are compared with the direct identification method. High order FIR models are used as process models to avoid bias issues arising from inadequate model structures for the processes. Comparisons are, therefore, made based on the variance of the identified process models both for asymptotic situations and for finite data sets. Process model bias resulting from improper selection of the noise and sensitivity function models is also investigated. In this context, the results support the use of direct identification methods on closed-loop data.     

纺织品检测中的模式识别应用

将模式识别方法用于毛巾和纺织面料生产过程中的瑕点检测, 研究了模糊小波模式识别方法, 对毛巾生产过程的多种瑕点监测进行了算法分析和简要论述, 这种算法具有更强的实用性和鲁棒性. 又由于系统采用DSP实现, 使识别速度大大提高, 完全能满足实时性的要求.     

A new kernel-based approach for linear system identification

This paper describes a new kernel-based approach for linear system identification of stable systems. We model the impulse response as the realization of a Gaussian process whose statistics, differently from previously adopted priors, include information not only on smoothness but also on BIBO-stability. The associated autocovariance defines what we call a stable spline kernel. The corresponding minimum variance estimate belongs to a reproducing kernel Hilbert space which is spectrally characterized. Compared to parametric identification techniques, the impulse response of the system is searched for within an infinite-dimensional space, dense in the space of continuous functions. Overparametrization is avoided by tuning few hyperparameters via marginal likelihood maximization. The proposed approach may prove particularly useful in the context of robust identification in order to obtain reduced order models by exploiting a two-step procedure that projects the nonparametric estimate onto the space of nominal models. The continuous-time derivation immediately extends to the discrete-time case. On several continuous- and discrete-time benchmarks taken from the literature the proposed approach compares very favorably with the existing parametric and nonparametric techniques.     

A method of sensor fault detection and identification

A method of Bayesian belief network (BBN)-based sensor fault detection and identification is presented. It is applicable to processes operating in transient or at steady-state. A single-sensor BBN model with adaptable nodes is used to handle cases in which process is in transient. The single-sensor BBN model is used as a building block to develop a multi-stage BBN model for all sensors in the process under consideration. In the context of BBN, conditional probability data represents correlation between process measurable variables. For a multi-stage BBN model, the conditional probability data should be available at each time instant during transient periods. This requires generating and processing a massive data bank that reduces computational efficiency. This paper presents a method that reduces the size of the required conditional probability data to one set. The method improves the computational efficiency without sacrificing detection and identification effectiveness. It is applicable to model- and data-driven techniques of generating conditional probability data. Therefore, there is no limitation on the source of process information. Through real-time operation and simulation of two processes, the application and performance of the proposed BBN method are shown. Detection and identification of different sensor fault types (bias, drift and noise) are presented. For one process, a first-principles model is used to generate the conditional probability data, while for the other, real-time process data (measurements) are used.     

Identification of the Tennessee Eastman challenge process with subspace methods

The Tennessee Eastman challenge process is a realistic simulation of a chemical process that has been widely used in process control studies. In this case study, several identification methods are examined and used to develop MIMO models that contain seven inputs and ten outputs. ARX and finite impulse response models are identified using reduced-rank regression techniques (PLS and CCR) and state-space models identified with prediction error methods and subspace algorithms. For a variety of reasons, the only successful models are the state-space models produced by two popular subspace algorithms, N4SID and canonical variate analysis (CVA). The CVA model is the most accurate. Important issues for identifying the Tennessee Eastman challenge process and comparisons between the subspace algorithms are also discussed.     

基于规则的粉螨亚目螨种分类鉴定专家系统

应用传统的分类检索方法进行粉螨亚目螨种的分类鉴定是一个复杂、繁琐的过程。随着人工智能技术的发展,采用专家系统技术编制螨种分类鉴定软件将有效地改变传统的分类鉴定方式。通过应用专家系统技术提炼出螨种的分类鉴定规则,设计了基于产生式的推理程序,并按照一定的推理控制策略实现了螨种的分类鉴定,满足了用户的需要,具有重要的实践价值。     

两步H∞辨识算法的一个近似最优的误差上界

利用逼近理论中的n-宽度和Bernstain不等式,以一般性的窗口系数为变量,对鲁棒 辨识中的两步H∞辨识算法,建立了一个近似最优的误差上界函数.该函数是窗口系数的凸 函数,它不仅可用于计算任意窗口系数对应的辨识误差上界,还为优化选择两步H∞ 辨识算 法的窗口系数提供了可行途径.     

Dynamical modelling and control of plastics extrusion processes

Alternative methods for determining plastics extrusion process models, suitable for high level control, are examined and the importance of time-series techniques for feedforward control is demonstrated. The results of extrusion process dynamic model identification experiments, carried out on a single screw extruder used for processing polyethylene, are described. Some results of exploratory control strategy simulations are included. Control of plastics melt pressure and temperature at the die is suggested as an effective indirect means of controlling die flow rate in most industrial situations.     

An adaptive robust M-estimator for nonparametric nonlinear system identification

An adaptive robust M-estimator for nonparametric nonlinear system identification is proposed. This M-estimator is optimal over a broad class of distributions in the sense of maximum likelihood estimation. The error distributions are described by the generalized exponential distribution family. It combines non-parametric regression techniques to form a powerful procedure for nonlinear system identification. The adaptive procedure's excellent performance characteristics are illustrated in a Monte Carlo study by comparing the results with previous methods.     

自适应插补控制器及其在时变系统中的应用

为提高时变系统的稳定性和精度,提出了基于在线参数辨识的自适应插补控制方法.首先,采用传递函数的即约分解方法,利用Bezout等式,导出了针对时变参数系统的插补控制器,并证明了插补控制系统的稳定性.这种插补控制器还可改形为常用PI形式,以方便工程上的应用.其次,引入在线辨识方法,构造出自适应插补控制器.最后,将自适应插补控制器应用于参数时变的胶带压延机厚度控制系统中,通过仿真证明了控制的有效性.     

Evaluating estimation of gain directionality : Part 2: A case study of binary distillation

The analysis method presented in part 1 of this paper is applied to case studies of binary distillation using the LV and DV control configurations. Based on this we conclude that insufficient estimation of the maximum and minimum process gains for multivariable systems, i.e. gain directionality, is caused by misalignment of inputs and outputs with the corresponding input and output singular vectors, together with a high condition number. This uncertainty has a significant impact on the achievable control performance of multivariable controllers designed from the identified model. For these classes of MIMO systems we show that the uncertainty in the estimate of gain directionality can be reduced by using multi-input perturbations or preferably closed-loop identification as opposed to single-input perturbations. In the case of closed-loop identification it is sufficient to use simple single-loop proportional control, designed from limited qualitative knowledge about the plant. For standard parametric identification commonly used model evaluation techniques, such as degrees of explanation and the quality of the estimated individual transfer functions, are compared to the methodology presented in part 1. As shown, these standard evaluation techniques cannot be used to conclude whether the gain directionality has been adequately estimated. However, using the proposed method tight bounds are obtained on the quality of the estimated gain directionality. Furthermore, we illustrate that the quality of the estimate of gain directionality has a strong impact on achievable control performance, i.e. a significant error in the estimate of the low-gain direction in the plant will cause significant deterioration of the control performance whenever control action is required in this direction. Thus, by using proper scaling of the plant inputs and outputs the method presented analyses the model quality with respect to the control problem at hand.     

Identification of a simulated Shell distillation column using time series analysis methods

This paper presents details of a multivariate time series identification of the simulated Shell distillation column described in the introductory paper by Cott. The approach applied here involves the use of time series identification techniques and may be considered as representative of the system identification procedures adopted by Shell Canada for quadratic dynamic matrix control (QDMC) applications. Results indicate that techniques of time series analysis are very flexible and capable of producing satisfactory step response models under both low and high signal to noise conditions.     

Regressor and structure selection in NARX models using a structured ANOVA approach

Regressor selection can be viewed as the first step in the system identification process. The benefits of finding good regressors before estimating complex models are especially clear for nonlinear systems, where the class of possible models is huge. In this article, a structured way of using the tool analysis of variance (ANOVA) is presented and used for NARX model (nonlinear autoregressive model with exogenous input) identification with many candidate regressors.     

Closed-loop identification condition for ARMAX models using routine operating data

In industrial process control engineering, using routine operating data obtained from closed-loop operation of a process for model identification would be extremely valuable in applications such as control performance monitoring, root cause diagnosis, and controller retuning. However, the conditions for closed-loop identifiability using routine operating data are still largely unknown or untried. In this paper, criteria for closed-loop identification of an autoregressive, moving average process with exogenous input (ARMAX) regulated with an arbitrary, rational, polynomial controller are derived. The theoretical criteria that are developed for the closed-loop identification of an ARMAX process are compared with Monte Carlo simulations and previous theoretical results. It is shown that the newly-proposed theoretical results are in agreement with the simulation results.     

Applying hierarchical task analysis to improving the patient positioning for direct lateral interbody fusion in spinal surgery

The present study brings together for the first time the techniques of hierarchical task analysis (HTA), human error identification (HEI), and business process management (BPM) to select practices that can eliminate or reduce potential errors in a surgical setting. We applied the above approaches to the improvement of the patient positioning process for lumbar spine surgery referred to as ‘direct lateral interbody fusion’ (DLIF). Observations were conducted to gain knowledge on current DLIF positioning practices, and an HTA was constructed. Potential errors associated with the practices specific to DLIF patient positioning were identified. Based on literature review and expert views alternative practices are proposed aimed at improving the DLIF patient positioning process. To our knowledge, this is the first attempt to use BPM in association with HEI/HTA for the purpose of improving the performance and safety of a surgical process – with promising results.