Process situation assessment: From a fuzzy partition to a finite state machine

Process situation assessment plays a major role in supervision of complex systems. The knowledge of the system behavior is relevant to support operators in their decision tasks. For complex industrial processes such as chemical or petrochemical ones, most of supervision approaches are based on data acquisition techniques and specifically on clustering methods to cope with the difficulty of modeling the process. Consequently, the system behavior can be characterized by a state space partition. This way, situation assessment is performed online through the tracking of the system evolution from one class to another. Furthermore, a finite state machine that is a support tool for process operators is elaborated to model the system behavior. This article presents theoretical aspects according to which the intuition that the trajectory observation of a dynamical system by a sequence of classes, to which the actual state belongs, gives valuable information about the real behavior of the system is substantiated. Thus, practical aspects are developed on the state machine construction and illustrated by two simple applications in the domain of chemical processes.     

Fuzzy behavior hierarchies for multi‐robot control

Hierarchical approaches and methodologies are commonly used for control system design and synthesis. Well‐known model‐based techniques are often applied to solve problems of complex and large‐scale control systems. The general philosophy of decomposing control problems into modular and more manageable subsystem control problems applies equally to the growing domain of intelligent and autonomous systems. However, for this class of systems, new techniques for subsystem coordination and overall system control are often required. This article presents an approach to hierarchical control design and synthesis for the case where the collection of subsystems is comprised of fuzzy logic controllers and fuzzy knowledge‐based decision systems. The approach is used to implement hierarchical behavior‐based controllers for autonomous navigation of one or more mobile robots. Theoretical details of the approach are presented, followed by discussions of practical design and implementation issues. Example implementations realized on various physical mobile robots are described to demonstrate how the techniques may be applied in practical applications involving homogeneous and heterogeneous robot teams. © 2002 Wiley Periodicals, Inc.     

Process identification, uncertainty characterisation and robustness analysis of a pilot-scale distillation column

In this paper different approaches for developing robust advanced control techniques are investigated. A pilot-scale distillation column connected to an industrial distributed control system (ABB MOD 300) that in turn has been interfaced to a VAX-cluster through an Ethernet Gateway is used as a pseudo-industrial set-up to perform these studies. A novel robust multivariable, low order, high performance, model based controller was designed and implemented as a standard PID block within the distributed control system. To provide a systematic approach for designing such an advanced robust controller, several techniques such as dynamic modelling, system identification, uncertainty identification and characterisation etc., are incorporated. The problem of uncertainty characterisation is fully addressed from both theoretical and practical point of view. Both structured and highly structured uncertainty characterisation approaches are used to investigate the robust stability and performance of the control system. Several practical techniques are proposed for designing a robust model-based controller that are readily applicable in an industrial environment. The paper is accompanied by several simulations and also experimental evidences which demonstrate the effectiveness of the proposed approach. ©     

Adaptive Controller Design for Unknown Systems Using Measured Data

This paper presents a measurement‐based adaptive control design approach for unknown systems working over a wide range of operating conditions. Traditional control design approaches usually require the availability of a mathematical model. However, it has been shown in many practical situations that, due to complex dynamics of physical systems, some simplifying assumptions are made for the derivation of mathematical models. Hence, controller design based on simplified models may result in degradation of the desired closed‐loop performance. Data‐based control design approaches can be viewed as an alternative approach to model‐based methods. Most data‐based control methods available in the literature aim to design controllers for unknown systems that operate only at a given operating point. However, the dynamical behavior of plants may change for different operating conditions, which makes the task of designing a controller that works over the entire range of operating conditions more challenging. In this paper, we address such a problem and propose to design adaptive controllers based on measured data. Such a proposed method is based on designing a set of measurement‐based controllers validated at a finite set of pre‐specified operating points. Then, the parameters of the adaptive controller are obtained by interpolating between the set of pre‐designed controller parameters to derive a gain‐scheduling controller. Moreover, low‐order adaptive controllers can be designed by simply selecting the desired controller structure. The efficacy of the proposed approach is experimentally validated through a practical application to control a heating system operated over a large range of flow rate.     

Frequency- and time-domain methods for the numerical modeling of full-bridge aeroelasticity

A numerical framework for full-bridge aeroelasticity is presented, based on unsteady cross-sectional load models and on the finite-element modeling of the structure. A frequency-domain approach based on aeroelastic derivatives and nonlinear complex eigenvalue analysis is compared to its equivalent time-domain counterpart based on indicial functions and direct integration of the equations of motion. A version of the time-domain load model consistent with the quasi-steady limit behavior is developed and a procedure for the numerical identification of the indicial functions from measured aeroelastic derivatives is presented. The aeroelastic stability analysis is chosen as benchmark. A numerical example is offered where the equivalency of the two approaches is proved for a full-bridge model. Advantages and disadvantages of the two techniques are discussed.     

Iterative motion feedforward tuning: A data-driven approach based on instrumental variable identification

Feedforward control can significantly enhance the performance of motion systems through compensation of known disturbances. This paper aims to develop a new procedure to tune a feedforward controller based on measured data obtained in finite time tasks. Hereto, a suitable feedforward parametrization is introduced that provides good extrapolation properties for a class of reference signals. Next, connections with closed-loop system identification are established. In particular, instrumental variables, which have been proven very useful in closed-loop system identification, are selected to tune the feedforward controller. These instrumental variables closely resemble traditional engineering tuning practice. In contrast to pre-existing approaches, the feedforward controller can be updated after each task, irrespective of noise acting on the system. Experimental results confirm the practical relevance of the proposed method.     

An innovative approach for automatic generation,verification and optimization of part programs in turning

Continuous innovation of products and optimization of manufacturing processes are of fundamental importance for preserving competitiveness. In the last decades, several approaches based on analytic models for optimization of basic machining operations such as cylindrical turning and face milling have been developed. However, the analytic approaches may not be adequate for real industrial applications, since they are based on average cutting parameters and thus they are not capable of taking into account the effect of complex geometries and instantaneous cutting conditions. In this paper, an innovative integrated system for automatic generation of optimized part programs in turning based on realistic machining simulation is proposed. The system components are described in detail and the machining simulator is validated by comparison with the results of real cutting tests. Then, the optimization approach is applied to a simple case study. The results show that the behavior of the cost function is rather complex, even for simple workpieces. Moreover, the simulator can detect unfeasible combinations of cutting parameters and thus reduce inline part program refinement and optimization. The optimal combination of cutting parameters determined by the new system was competitive with the solutions derived from tool specifications or proposed by a machining expert.     

改进的卡尔曼滤波算法系统参数辨识仿真研究

研究系统参数辨识精度提高问题。辨识是从实验数据中提取有关系统信息的过程,由于存在噪声影响辨识精度,针对传统的卡尔曼滤波算法不能很好地提高跟踪精度且算法复杂的缺陷,为了解决实际系统辨识中参数噪声方差和观测噪声方差未知的等相关问题,提出了一种改进的无味卡尔曼滤波算法系统参数辨识方法,仿真结果表明,算法具有更好的泛化能力,在复杂的系统负载等情况下,也可以对系统的参数精确有效的进行辨识,验证了该算法是一种有效适用的系统参数辨识方法。     

Trends in identification

The paper refers to methods used for identification of linear and nonlinear systems. Deterministic and stochastic approaches are distinguished and specific features concerning parameters, structure and state estimation are briefly discussed from the point of view of possible advantages and difficulties for identification. Attention is paid to different final goals of identification with respect to the convenience of the methods in question. The most important trends in identification approaches are argued by unsolved problems of identification, by the complexity of numerical calculations and of practical applications. The significance of the uncertainty in structure, parameters or noise and the possible application of the a priori knowledge of the analysed system are taken into consideration.     

A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study

Model-based control strategies are widely used for optimal operation of chemical processes to respond to the increasing performance demands in the chemical industry. Yet, obtaining accurate models to describe the inherently nonlinear, time-varying dynamics of chemical processes remains a challenge in most model-based control applications. This paper reviews data-driven, Linear Parameter-Varying (LPV) modeling approaches for process systems by exploring and comparing various identification methods on a high-purity distillation column case study. Several LPV identification methods that utilize input–output and series expansion model structures are explored. Two LPV identification perspectives are adopted: (i) the local approach, which corresponds to the interpolation of Linear Time-Invariant (LTI) models identified at different steady-state operating points of the system and (ii) the global approach, where a parametrized LPV model structure is identified directly using a global data set with varying operating points. For the local approach, various model interpolation schemes are studied under an Output Error (OE) noise setting, whereas in the global case, a polynomial parametrization based OE prediction error minimization approach, an Orthonormal Basis Functions (OBFs) based model estimator and a Least-Square Support Vector Machine (LS-SVM) based non-parametric approach are investigated. Through extensive simulation studies, the aforementioned LPV identification approaches are analyzed in terms of the attainable model accuracy and local frequency response behavior of the obtained models. Recommendations are provided to achieve adequate choice between the methods for a particular process system at hand.     

Identification of abnormal events by data monitoring: Application to complex systems

Many maintenance actions, such as mechanical, electrical, and hydraulic skills, are mandatory to maintain complex systems in operational conditions. Considerable research has been conducted in these fields to optimize maintenance actions. Most research proposes approaches based on physics: physical model of a specific failure, law of aging, etc. In spite of their performance, these approaches are quite difficult to implement on a complex integrated system. Each field of expertise assesses the good health of a system part using its own experts, its own methods, and, in some cases, its own data. Nevertheless, these fields all make up the same machine, and no interaction between systems is considered. Our study is not based on physical approaches but uses operational data and mathematical tools to diagnose, off-line, the current state of the system. The proposed paper concerns a new concept consisting in characterizing normal system functioning by using data recorded during monitoring. The life profile of this complex system is described by employing all the available data to determine, on the one hand, all normal events and, on the other, to identify abnormal events according to their position compared to the normal envelope defined. The recorded data are then specifically analyzed to characterize the level of criticism of an event considered to be abnormal. This abnormal event could then be assimilated to a global behavioral drift of the studied behavior, which is different to usual behavior. This approach is applied to helicopters by use of all flight recorded data.     

Discovering expressive process models by clustering log traces

Process mining techniques have recently received notable attention in the literature; for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Following this line of research, the paper investigates an extension of such basic approaches, where the identification of different variants for the process is explicitly accounted for, based on the clustering of log traces. Indeed, modeling each group of similar executions with a different schema allows us to single out "conformant" models, which, specifically, minimize the number of modeled enactments that are extraneous to the process semantics. Therefore, a novel process mining framework is introduced and some relevant computational issues are deeply studied. As finding an exact solution to such an enhanced process mining problem is proven to require high computational costs, in most practical cases, a greedy approach is devised. This is founded on an iterative, hierarchical, refinement of the process model, where, at each step, traces sharing similar behavior patterns are clustered together and equipped with a specialized schema. The algorithm guarantees that each refinement leads to an increasingly sound mDdel, thus attaining a monotonic search. Experimental results evidence the validity of the approach with respect to both effectiveness and scalability.     

B-spline neural network design using improved differential evolution for identification of an experimental nonlinear process

B-Spline Neural Network (BSNN), a type of basis function neural network, is trained by gradient-based methods which may fall into local minima during the learning procedure. To overcome the limitations encountered by gradient-based optimization methods, we propose differential evolution (DE) – an evolutionary computation methodology – which can provide a stochastic search to adjust the control points of a BSNN. In this paper, we propose six DE approaches using chaotic sequences based on logistic mapping to train a BSNN. Chaos describes the complex behavior of a nonlinear deterministic system. The application of chaotic sequences instead of random sequences in DE is a powerful strategy to diversify the DE population and improve the DE's performance in preventing premature convergence to local minima. The numerical results presented here indicate that chaotic DE was effective for building a good BSNN model for the nonlinear identification of an experimental nonlinear yo–yo motion control system.     

A user perspective on errors-in-variables methods in system identification

The paper starts with a brief survey of errors-in-variables methods in system identification. Background and motivation are given, and it is illustrated why the identification problem can be difficult. Under general weak assumptions, the system is not identifiable, but can be parameterized using one degree of freedom. Examples where identifiability is achieved under additional assumptions are also provided. A number of approaches for parameter estimation of errors-in-variables models are reviewed. The underlying assumptions and principles for each approach are highlighted. The paper then continues by discussing from a user’s perspective on how to proceed when practical problems are handled.     

Hammerstein-Wiener nonlinear model based predictive control for relative QoS performance and resource management of software systems

Runtime management of Quality of Service (QoS) performance and resource provisioning is a vital issue in shared resource software environments. A useful performance management technique for such software systems is the relative guarantee feedback control scheme. The existing approaches for this class of control systems are mainly based on off-line linear or on-line model identification and control techniques, which tend to have performance issues in the presence of nonlinearities induced by this scheme. Instead of using such modeling techniques, this paper proposes a new approach for QoS performance management and resource provisioning by using an off-line identification of Hammerstein and Wiener nonlinear block structural model. Using the characteristic structure of the nonlinear model, a predictive feedback controller based on a gain schedule technique is incorporated in the design to achieve the performance objectives. The proposed approach is validated using experiments based on a prototype, demonstrating superior runtime QoS performance management and resource provisioning in a complex software system.     

Mathematical modeling of blood circulation system and its practical application

The human blood circulation system is represented by a nonlinear oscillation system for computer-aided digital modeling in real time scale. A parametric identification problem is formulated and its numerical solution algorithm is designed. A computer-aided blood circulation modeling and identification system is designed. The new approaches to construct real control systems for artificial and auxiliary blood circulation elements are based on neurocomputer technologies.     

Fault diagnosis based on identified discrete-event models

Fault diagnosis of Discrete-Event Systems consists of detecting and isolating the occurrence of faults within a bounded number of event occurrences. Recently, a new model for discrete-event system identification with the aim of fault detection, called Deterministic Automaton with Outputs and Conditional Transitions (DAOCT), has been proposed in the literature. The model is computed from observed fault-free paths, and represents the fault-free system behavior. In order to obtain compact models, loops are introduced in the model, which implies that sequences that are not observed can be generated leading to an exceeding language. This exceeding language is associated with possible non-detectable faults, and must be reduced in order to use the model for fault detection. After detecting the fault occurrence, its isolation is carried out by analyzing residuals. In this paper, we present a fault diagnosis scheme based on the DAOCT model. We show that the proposed fault diagnosis scheme is more efficient than other approaches proposed in the literature, in the sense that the exceeding language can be drastically reduced, reducing the number of non-detectable fault occurrences, and, in some cases, reducing also the delay for fault diagnosis. A practical example, consisting of a plant simulated by using a 3D simulation software controlled by a Programmable Logic Controller, is used to illustrate the results of the paper.     

CAS理论视角下产业集群演化模型构建

针对产业集群的复杂自适应系统（Complex Adaptive System,CAS）特性,以CAS理论的个体行为模型和整体演化的ECHO模型为基础,并结合产业集群的概念特征建立产业集群系统构架。并在此基础上,提出产业集群系统动态演化模型。该模型把产业集群演化的生命周期特性和复杂系统的演化识别过程结合起来,以复杂系统的演化机制为依据,认为产业集群诞生于混沌经济环境,经过系统对环境的自适应学习,自身的分岔与突变,系统功能的整体涌现,达到系统暂态稳定,是一个螺旋上升的循环过程。产业集群系统演化的核心机制是以产业分工为主的自适应机制和以系统创新为主的分岔与突变机制。     

A comparison of networked approximators in parallel mode identification of a bioreactor

This paper presents a simulation based comparison of Multilayer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Least Squares Support Vector Machines (LS-SVM) in parallel mode identification of a chemical process displaying several challenges. The paper provides a graphical analysis of the nonlinear behavior for the system under investigation, a case study of purely parallel identification scheme, the effects of noise in the training data on the prediction performance and the performance comparison of the standard approaches under limited amount of numerical data. The results have shown that the emulators utilizing the MLP structure are superior to the others in terms of predicting the system trajectories, locating the limit cycle, noise driven response and predicting the steady state conditions given only 582 pairs of training data. Furthermore, as opposed to others, with the MLP structure, these qualities disappear smoothly as the noise level is increased gradually.     

平稳过程突变的在线检测与辨识

将稳健－ 容错辨识与突变检测技术相结合,在合理给出平稳过程位置－ 刻度参数容错辨识算法的基础上,构造了一组可靠实用的过程脉冲型故障在线检测与辨识算法。通过过程自差分处理,将平稳过程阶跃型突变转化为过程脉冲型突变,为利用脉冲型故障检测与辨识方法处理阶跃型突变问题建立了联系。仿真计算证实了所建立的检测与辨识算法的有效性