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Über dieses Buch

Control Performance Management in Industrial Automation provides a coherent and self-contained treatment of a group of methods and applications of burgeoning importance to the detection and solution of problems with control loops that are vital in maintaining product quality, operational safety, and efficiency of material and energy consumption in the process industries. The monograph deals with all aspects of control performance management (CPM), from controller assessment (minimum-variance-control-based and advanced methods), to detection and diagnosis of control loop problems (process non-linearities, oscillations, actuator faults), to the improvement of control performance (maintenance, re-design of loop components, automatic controller re-tuning). It provides a contribution towards the development and application of completely self-contained and automatic methodologies in the field. Moreover, within this work, many CPM tools have been developed that goes far beyond available CPM packages.

Control Performance Management in Industrial Automation:

· presents a comprehensive review of control performance assessment methods;

· develops methods and procedures for the detection and diagnosis of the root-causes of poor performance in complex control loops;

· covers important issues that arise when applying these assessment and diagnosis methods;

· recommends new approaches and techniques for the optimization of control loop performance based on the results of the control performance stage; and

· offers illustrative examples and industrial case studies drawn from – chemicals, building, mining, pulp and paper, mineral and metal processing industries.

This book will be of interest to academic and industrial staff working on control systems design, maintenance or optimisation in all process industries.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
This chapter gives a brief introduction to the objectives, principle, tasks, and challenges of control performance management (CPM). A basic procedure for CPM, assessment benchmarks, are given. The key dates of the development of CPM technology and a literature survey are described.
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Evaluation of the Level of Control Performance

Frontmatter

Chapter 2. Assessment Based on Minimum-Variance Principles

Abstract
The standard control performance assessment methods are based on the minimum-variance (MV) principle or modifications of it. The key point is that the MV benchmark (as a reference performance bound) can be estimated from routine operating data without additional experiments, provided that the system delay is known or can be estimated with sufficient accuracy. The main focus of the chapter is on presenting assessment methods based on minimum-variance control (MVC) for single feedback control and for combined feedback and feedforward control loops. The extension of MV assessment to the assessment of set-point tracking and cascade control is also provided. All methods presented are illustrated using many examples.
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Chapter 3. User-Specified Benchmarking

Abstract
Minimum-variance benchmarking only considers the most fundamental performance limitation of a control loop owing to the existence of time delays. In practice, however, there are many other limitations on the achievable control performance, such as constraints on controller order, structure, and action. Many researchers have introduced modified/extended versions of the Harris index to include design specifications of the user (such as the rise time and settling time) and take into account time delays in the system, leading to more realistic performance indices, referred to as user-specified benchmarks. This chapter provides a general setting for user-specified performance assessment and then presents the IMC-achievable performance assessment, the extended-horizon approach, performance assessment based on desired pole location, historical benchmarking, and assessment methods based on reference models.
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Chapter 4. Advanced Control Performance Assessment

Abstract
This chapter deals with extensions of the MV benchmark that need substantially more information about the plant than just the time delay. An extension of the MV benchmark is the approach of generalised MV (GMV) benchmarking, minimising a weighted sum of the control error and control effort. More general but rigorous extensions are the linear-quadratic Gaussian (LQG) benchmark and the model-predictive control (MPC) assessment. These benchmarks are useful when more information on controller performance, such as how much can the output variance be reduced without significantly affecting the controller output variance is needed, or for cases where actuator wear is a concern. This chapter provides an overview of these advanced methods. It is particularly shown how to use routine operating data to distinguish between poor performance due to plant–model mismatch and that due to improper tuning of the MPC controller. Moreover, performance measures that estimate potential benefit from re-identification of the process model or re-tuning of the controller are introduced. This is essential in MPC monitoring, as a process model is a substantial component of the MPC controller.
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Chapter 5. Deterministic Controller Assessment

Abstract
Systems in some industrial fields, such as power and servo industry and robotics, show references and disturbances tending to be more deterministic rather than stochastic. In these cases, the processes are operated with frequent changes in the references and hence output levels. So, for such systems, deterministic assessment techniques are needed. This chapter presents three methods for control performance assessment based on deterministic criteria: settling time and IAE indices gained from set-point response data, the idle index for detection of sluggish control, and the area index for evaluating deterministic load-disturbance rejection performance. These techniques are compared and discussed using simulation examples.
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Chapter 6. Minimum-Variance Assessment of Multivariable Control Systems

Abstract
Since the loops in multivariable control systems can be coupled, a multivariable control strategy can further reduce process variations, thus, only multivariable assessment can provide the right measure of performance improvement potential in the general case. In this chapter, methods for multivariable minimum-variance benchmarking are presented: it is shown how to use the interactor matrix to derive the multivariable variant of MVC; then the FCOR algorithm as the most known algorithm for assessing MIMO control systems based on routine operating data and the knowledge of the interactor matrix is presented. As the interactor matrix is hard to determine, and thus control assessment based on it is difficult, an assessment procedure that does not require the interactor matrix is proposed. Numerous examples are given to illustrate how the methods work.
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Chapter 7. Selection of Key Factors and Parameters in Assessment Algorithms

Abstract
Performance assessment algorithms contain many options and parameters that must be specified by the user. These factors substantially affect the accuracy and acceptability of the results of assessment exercises. A fundamental basis for performance assessment is to record and carefully inspect suitable closed-loop data. Pre-processing operations, which are suggested and those which should be strictly avoided, are given in this chapter. The first decision in control performance assessment is the choice of a (time-series) model structure for describing the net dynamic response associated with the control error. There are different possible structures and different possible identification techniques. The most widely used of them are briefly described. Particularly for MV and GMV benchmarking, it is decisive to properly select or estimate the parameters’ time delay and model orders. This topic is discussed, and some of the basic models and identification techniques concerning assessment accuracy and computational load are compared, to provide suggestions of the best suited approaches to be applied in practice.
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Detection and Diagnosis of Control Performance Problems

Frontmatter

Chapter 8. Statistical Process Control

Abstract
The aim of statistical process control (SPC) techniques, when applied to control-loop operating data, is to identify and track certain variation within the loop, and thus highlight situations that show abnormal behaviour, i.e. statistically significant events or abnormalities. Indeed, understanding this variation may be a first step towards the improvement of controller performance. Since variation is present in any process, deciding when the variation is natural and when it needs correction is the key to monitor control performance using SPC. Today, SPC has become more than control charting alone; it is an umbrella term for the set of activities and methods for data analysis and quality control. This chapter contains a brief description of selected univariate and multivariate SPC techniques.
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Chapter 9. Detection of Oscillating Control Loops

Abstract
Since a control loop may exhibit poor performance for various reasons, it is not only important to detect poor performance, but the challenge is to trace the bad performance to its root cause. Not only controller design and tuning but also other elements in the control systems, such as sensors and actuators, are often responsible for the poor performance. There are many reasons for poor control performance, which can be detected using specialised methods and indices, without requiring the knowledge of time delays or model identification. Attempt of this chapter to review and suggest procedures for semi- or fully automatic detection of oscillating control loops. For each method described, the basic assumptions, limitations, strengths and weaknesses will be clearly stated. The parameterisation of the methods is thoroughly discussed to give default settings for real applications. Industrial examples from different industrial fields (chemicals, refining, petro-chemicals, pulp & paper, mining, mineral and metal processing) are presented throughout the chapters to demonstrate the applicability of the methods.
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Chapter 10. Detection of Loop Nonlinearities

Abstract
Most of control performance methods assume that the system is (at least locally) linear. However, the presence of certain type of nonlinearity may cause severe performance problems. For instance, stiction, hysteresis and dead-band in actuators, or faulty sensors can induce unwanted oscillations. Thus, it is recommended to evaluate “how linear (or nonlinear)” the closed loop under consideration actually is in an early step of the assessment procedure. Features of nonlinear behaviour can be used as a basis for the development of some nonlinearity detection methods. The exploitation of the bicoherence property led to the bicoherence technique. Alternatively, the surrogate analysis method, which is based on the regularity of phase patterns in nonlinear time series, can be used. Both methods are presented in this chapter, and a comparative study is given on some industrial data sets.
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Chapter 11. Diagnosis of Stiction-Related Actuator Problems

Abstract
Control valves are the most commonly used actuators or final control elements in the process industries. Many surveys indicate that about 20–30 % of all control loops oscillate due to valve problems caused by valve nonlinearities, such as stiction, hysteresis, dead-band or dead-zone. Many control loops in process plants perform poorly due to valve static friction (stiction) as one of the most common equipment problems. Valve stiction in control loops causes oscillations in form of limit cycles. This phenomenon increases variability in product quality, accelerates equipment wear, or leads to control system instability. This chapter is devoted to the illustration of the actuator stiction effect on control-loop performance and to a review of the most important techniques for automatic stiction detection, to be incorporated in performance monitoring. A basic oscillation diagnosis procedure is then proposed.
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Chapter 12. Complete Oscillation Diagnosis Based on Hammerstein Modelling

Abstract
When a control loop is detected to be oscillating, the root cause of that oscillation may be aggressive controller tuning, external disturbance, or valve nonlinearity, particularly stiction. Even in the case where stiction is detected by applying any of the techniques described in Chap. 11, it is desirable to quantify the extent of stiction. This chapter presents a novel technique for detection and estimation of valve stiction in control loops from normal closed-loop operating data based on a two-stage identification algorithm. The control system is represented by a Hammerstein model including a two-parameter stiction model and a linear model for describing the remaining system part. Only OP and PV data are required for the proposed technique. This not only detects the presence of stiction but also provides estimates of the stiction parameters. Therefore, the method is useful in short-listing a large number of control valves more or less suffering from stiction in chemical or other plants, containing hundreds or thousands control loops. This helps reduce the plant maintenance cost and increase the overall profitability of the plant. A unique feature of the proposed technique is also its capability to discriminate between the different oscillation sources or detect the situation where the loop suffers from two or more oscillation root causes simultaneously.
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Performance Improvement

Frontmatter

Chapter 13. Performance Monitoring and Improvement Strategies

Abstract
The final and most challenging objective of applying performance assessment methods and indices should always be to suggest measures to improve the control or process/plant performance. Even in the case where the loop is found to work at acceptable performance level, it is useful to know how the performance can be improved to attain top level. In this chapter, possible performance improvement measures are briefly discussed. Some paradigms and strategies for monitoring the performance of complex process-control systems are introduced. A comprehensive control-performance assessment procedure is proposed combining different methods described throughout the previous chapters of the book.
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Chapter 14. Controller Auto-Tuning Based on Control Performance Monitoring

Abstract
In practice, it is the norm to perform controller tuning only at the commissioning stage and never again. A control loop that worked well at one time is prone to degradation over time unless regular maintenance is undertaken. Typically, 30 % of industrial loops have poor tuning, and 85 % of loops have sub-optimal tuning. There are many reasons for the degradation of control loop performance, including changes in disturbance characteristics, interaction with other loops, changes in production characteristics (e.g. plant throughput, product grade), etc. Also, many loops are still “tuned by feel” without considering appropriate tuning methods—a practice often leading to very strange controller behaviour. This chapter presents new tuning methods that treat controller tuning in the context of control performance monitoring and thus substantially extend the traditional field of controller auto-tuning. This means that control performance measures are continuously monitored on a regular basis, i.e. during normal operation, and performance statistics used to schedule loop retuning and automatically determine the optimal controller parameters. It starts with recalling the basic concepts of PID auto-tuning and adaptation as well as a classification of CPM-based controller re-tuning methods. Techniques that deliver optimal controller parameters by solving an optimisation problem are then described. Subsequently new re-tuning methods are presented, which simultaneously provide the assessment of the controller performance and finding the optimal controller settings in an iterative way on the closed loop. Simulation studies are presented to compare the different techniques and make suggestions for using them.
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Applications and Tools

Frontmatter

Chapter 15. Industrial CPM Technology and Applications

Abstract
The growing acceptance of the CPM technology in some industries is due to the awareness that control software is recognised to be a capital asset that should be maintained, monitored and revised routinely. Control systems permanently showing top performance significantly reduce or even avoid product-quality degradation, loss of energy resources, waste of production, lost production time and shortened lifetimes for plant components. The CPM field has now matured to the point where a large number of industrial applications and some commercial algorithms and/or vendor services are available for control performance auditing or monitoring. The primary purpose of this chapter is to present an overview of CPM industrial applications and (available) software products. A comprehensive overview of published CPM applications to industrial processes and commercially available CPM products are given.
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Chapter 16. Performance Monitoring of Metal Processing Control Systems

Abstract
Numerous investigations have shown that the performance of control systems in the process industries is not satisfactory. This particularly applies for the steel industry, where it is the norm to perform controller tuning only at the commissioning stage and then never again. A loop that worked well at one time is prone to degradation over time unless regular check and maintenance is undertaken. The field of metal processing continues to provide challenges in the application of process control and supervision at every level of the automation hierarchy, enterprise optimisation and system integration. Techniques successfully used in other process industries have to be adapted to the specific properties and conditions of steel processing, particularly rolling mills, showing high sample rates, varying time delays and semi-continuous operation. This chapter provides a contribution in this direction. Successfully completed industrial case studies and tailored CPM tools are presented. The studies involve the application of different CPM methods to different plants in the rolling area.
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Backmatter

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