Control Loop Performance Assessment Research Articles

Performance assessment of cascade control loops with non-Gaussian disturbances using entropy information

Cascade control is one of the routinely used control strategies in industrial processes because it can dramatically improve the performance of single-loop control, reducing both the maximum deviation and the integral error of the disturbance response. Currently, many control performance assessment methods of cascade control loops are developed based on the assumption that all the disturbances are subject to Gaussian distribution. However, in the practical condition, several disturbance sources occur in the manipulated variable or the upstream exhibits nonlinear behaviors. In this paper, a general and effective index of the performance assessment of the cascade control system subjected to the unknown disturbance distribution is proposed. Like the minimum variance control (MVC) design, the output variances of the primary and the secondary loops are decomposed into a cascade-invariant and a cascade-dependent term, but the estimated ARMA model for the cascade control loop based on the minimum entropy, instead of the minimum mean squares error, is developed for non-Gaussian disturbances. Unlike the MVC index, an innovative control performance index is given based on the information theory and the minimum entropy criterion. The index is informative and in agreement with the expected control knowledge. To elucidate wide applicability and effectiveness of the minimum entropy cascade control index, a simulation problem and a cascade control case of an oil refinery are applied. The comparison with MVC based cascade control is also included.

Simultaneous Control Loop Performance Assessment and Process Identification Based on Fractional Models

Control loop performance assessment procedures are established as key cornerstone of process optimization and monitoring. Unfortunately, many methods do not consider the maximum possible performance which can be achieved by the fixed structure controller installed in the loop (typically PID). In authors’ recent work such method was described. It was shown that - with respect to classical minimum variance method - the maximum performance is strongly influenced by the process normalized dead time. Consequently, the process model should be re-estimated during the plant operation. This paper describes technique which integrates continuous estimation of process model including normalized dead-time and control loop performance assessment. The process model is considered as fractional in order to fit well to distributed parameter systems appearing in chemical process control industries. All procedures are packed into advanced function blocks which are ready for real-time applications.

Time and Frequency Performance Assessment of IMC PI Control Loops

In this paper a methodology is presented to assess the performance of IMC PI controllers from closed-loop response data for a chosen setpoint signal. Time and frequency domains tools are used to assess the closed-loop performance. Simulation examples illustrate the methodology.

Minimum entropy-based performance assessment of feedback control loops subjected to non-Gaussian disturbances

Control performance assessment (CPA) is a useful tool to establish the quality of industrial feedback control loops. While many current CPA techniques are developed solely for the controlled systems under the assumption of the Gaussian disturbance, the conventional minimum variance control (MVC) approach would not be applied when the disturbance distribution is non-Gaussian. In this paper, based on the information theory and the minimum entropy criterion, a more general CPA index for the feedback control loop subjected to unknown disturbance distribution is investigated. The fundamentals of MVC are first reexamined, and then an innovative performance index is given by incorporating the entropy. The feedback control algorithm based on minimum entropy, called MEC (minimum entropy control), is derived. MEC based CPA for the controlled system requires effective and systematic identification of the associated system models based on the closed-loop data. In this work, a new methodology based on the entropy criterion instead of the mean square error criterion is presented to estimate disturbances for the purpose of evaluating the performance of the control systems. To demonstrate the effective MEC based CPA method, both numerical and industrial examples are applied and compared with the MVC based CPA method.

Bode-like control loop performance index evaluated for a class of fractional-order processes

Control loop performance assessment techniques are crucial for optimizing any plant or machine. They can bring huge energy and material savings and increase product quality. Usually, the performance is compared to minimum variance controller. It is known that when optimizing process controllers having fixed structure (e.g. PIDs) different concepts must be applied. In this paper, the systematic approach for a class of fractional-order processes is presented. Inspired by the model free design techniques, only a minimum a priori information about the process is assumed. A novel performance index based on 'ideal' shape of sensitivity function is proposed. Two fundamental limits are considered: available loop bandwidth and robustness MS-index. The best possible performance is computed for all processes belonging to the fractional-order model set controlled by PID-type controllers. The authors believe that the presented ideas may be utilized by both academic and industrial sphere.

Control loop diagnosis with ambiguous historical operating modes: Part 1. A proportional parametrization approach

Control-loop performance assessment methods have been evolving over the past two decades, with many different monitor algorithms being used to single out specific problems and determine the operating mode. However, a change in operating mode may affect multiple monitors, resulting in the possibility of conflicting assessments. Data-driven Bayesian methods were previously proposed which use multiple monitors to yield probabilistic assessments; however, training data for Bayesian methods requires complete knowledge of underlying operational modes. This paper proposes an approach based on proportionality parameters θ to address the problem of incomplete mode information in the training data; values in θ can be used to fill in missing information, and by varying θ one can determine the boundaries on a probabilistic diagnosis. Two diagnostic approaches are considered: the first type is direct probability approach, which can only be applied when historical data on the operation mode is sufficient and representative. The second type is the likelihood approach which can be applied to more general cases, including when the historical data is too limited to adequately represent mode frequency. In order to represent mode frequency, the likelihood approach takes into account prior probabilities of operating modes. The proposed methods are evaluated in two simulated chemical processes.

Control loop performance assessment using detrended fluctuation analysis (DFA)

A new index is developed for assessing the performance of a single-input single-output (SISO) linear feedback control loop. The proposed metric is a specific scaling of the generalized Hurst exponent, computed through the method of detrended fluctuation analysis (DFA). We refer to this scaled exponent as the Hurst index. The new method compares favorably with the widely used minimum variance index (MVI), with both indices showing similar trends under changes in controller tunings during closed-loop simulations. The main advantage of the Hurst index over the MVI and other existing performance measures is that its determination does not require a priori knowledge of any loop parameters. Instead, computation of the index relies solely upon process output data collected during routine plant operation. Therefore, this new technique could potentially allow engineers to more efficiently identify problematic control loops.

Performance assessment for the water level control system in steam generator of the nuclear power plant

The steam generator water level control system is the most important components of a nuclear power plant. The operating steam generator water level control system is increasingly recognized as a capital asset that should be routinely maintained and monitored. However, the control loop performance assessment is still an open problem; thus, the performance assessment technology will be brought into the steam generator water level control system in nuclear power plants. Performance assessment methods for the plant with stable and unstable zeros of two PI controller systems are developed at all specific power levels. The numerical examples will demonstrate the effectiveness of the proposed method.

Performance Assessment of Control Loops

Performance Assessment of Control Loops

Control loop performance assessment and improvement of an industrial hydrotreating unit and its economical benefits

This paper describes the CLPA (Control Loop Performance Assessment) and its improvement applied to an industrial hydrotreating unit (HDT) located at Alberto Pasqualini Refinery (REFAP S.A.). It is presented a review on regulatory control performance evaluation methods and the developed tool is described. The basic steps of the work methodology are also presented, with focus on the planned and implanted actions to improve the unit dynamical performance. Finishing the paper, the main results for a long-term industrial application are presented, including the unusual economic gains evaluation.

Effect of Cascade Tuning on Control Loop Performance Assessment

The effect of cascade tuning on control loop performance is analyzed in the framework of a monitoring system implemented in a refinery plant. Improper (too conservative) tuning of the inner loop may bring to ambiguous or apparently wrong verdicts on the evaluation of single loop performance. Starting from the evidence that operators actions can be different from suggestions given by the monitoring system, the effect of cascade controllers tuning is examined through the illustration of possible scenarios, generated in simulation with different tuning policies. Explanation of the observed behavior and general guidelines to assist operators in the procedure of controller retuning are given.

FREE INTERACTOR MATRIX METHOD FOR CONTROL PERFORMANCE ASSESSMENT OF MULTI-VARIATE SYSTEMS

In this paper, an alternative method for the assessment of multi-vitiate control loop performance with consider twocircumstances. First, known time delays between each pair of inputs and outputs, and second, without relying on any a priori knowledge about the process model or timedelays. The performance of the control loop is calculated from data driven autoregressive moving average (ARMA) and prediction error model. It is clear that the limited data in scalar measure used for performance assessment results tends to steady-state as time tends to infinity, but large number of samples gives risen in scalar measures and tends to infinity as time samples tends to infinity and therefore it becomes difficult to calculate the performance index. In this paper, the later problem is solved by considering initial part of scalar measures with steady value for next-to-next time samples to calculate the control-loop performance index which would be utilized to decide healthy working of the control loop. Simulation example is included to show the performance index of multi-variate control loop.

Simple Continuous-time Identification Method for PID Controlled Crushing Plant Processes

PID controllers are indisputably the most common controller type encountered in process control applications. Therefore, they have been quite well accepted for process control of crushing processes as well. However, successful tuning of a PID controller typically requires a model of the controllable process. This paper presents a novel approach for continuous-time low order transfer function identification on a closed single-input-single-output PID control loop. The proposed method is based on calculation of time integrals on controller input and output signals. The method is relatively simple, numerically straighforward and intuitively appealing as it simultaneously provides model parameters and criteria for control loop performance assessment.

Determining the state of a process control system: Current trends and future challenges

AbstractIn many industrial plants, multiple, interconnected control loops are common. Their maintenance and improvement requires detailed controller performance assessment to determine not only whether they are behaving well, but also to determine the potential cause of any observed problems. Techniques for performance assessment can be divided into two broad categories (1) performance assessment of regulatory control loops; and (2) performance assessment of supervisory control loops that evaluate the economic performance of advanced control strategies, such as model predictive control (MPC). A comprehensive review of the literature on the industrial applications of performance assessment, as well as some of the currently available software, is also presented. © 2011 Canadian Society for Chemical Engineering

Control Performance Assessment Subject to Multi-Objective User-Specified Performance Characteristics

User-specified benchmark has been used for control loop performance assessment. Structured closed-loop responses which are subject to various performance characteristics constraints are proposed in this work and served as a user-specified benchmarks against which the existing controller performance is compared. The performance characteristics constraints can be a mixture of variance upper bound, pole location, peak amplitude and model approximation constraint. An linear matrix inequality-based approach is used to formulate this multi-objective problem and a cone complementarity linearization algorithm is applied to find an optimal solution. Simulation studies and experimental results are provided to demonstrate application of the proposed benchmarks.

Performance Assessment of PID Control Loops based on IMC Tuning Rule

AbstractIn industrial feedback control loops, setpoint changes are often in the form of step, ramp or other general types, and controllers are usually restricted to the PID form. This paper establishes the lower bounds of integrated absolute errors (IAEs), based on the widely-used internal model control (IMC) principle, from closed-loop responses subject to these setpoint changes. Taking the lower bound as a benchmark, an IMC-IAE-based index is proposed to assess the performance of PID controllers. Numerical and experimental examples, as well as an industrial case study, are provided to verify the lower bound as the performance benchmark, and to illustrate the effectiveness of using the proposed index for performance assessment of PID control loops.

Consistency of noise covariance estimation in joint input–output closed-loop subspace identification with application in LQG benchmarking

The LQG trade-off curve has been used as a benchmark for control loop performance assessment. The subspace approach to estimating the LQG benchmark has been proposed in the literature which requires certain intermediate matrices in subspace identification as well as the covariance matrix of the noise. It is shown in this paper that many existing closed-loop identification methods do not give a consistent estimate of the noise covariance matrix. As a result, we propose an alternative subspace formulation for the joint input–output closed-loop identification for which the consistency of the required subspace matrices and noise covariance is guaranteed. Simulation studies and experimental results are provided to demonstrate the utility of the proposed method.

Quality index framework for plant-wide performance evaluation

The complexity of industrial production plants results in difficulties in obtaining an overall view of plant performance and in finding the weak links that deteriorate performance and product quality. While control loop performance assessment is a popular subject, few authors address the problem of combining low-level performance indices into subsystem and plant level indices. This paper presents a performance assessment framework enabling the creation of different views to plant performance. In the proposed method, low-level performance indices are scaled to the same interval so that their interpretation, comparison, and combination are more straightforward. Different methods for combining low-level indices to create subsystem and plant level performance measures are studied. Implementation in a large-scale industrial process is described.

Process Control Performance Assessment: From Theory to Implementation (Ordys, A.W. et al.; 2007) [Book reviews

This book is timely as a state-of-the-art summary on some of the most recent developments in control loop performance assessment. In reality, the book provides a broader context to control performance assessment other than merely evaluating the controller performance. What is also discussed includes business performance benchmarking, economic auditing of control systems, plantwide disturbance diagnosis, oscillation detection, and valve stiction monitoring, As such, this book covers topics beyond traditional control loop performance assessment. Overall, the book is well written. An excellent feature is that detailed step-by-step procedures for algorithm implementation are given, which can be a good user's guide. Extensive simulation and industrial examples are cited to illustrate the algorithms. The book provides a food balance between academic and engineering aspects. Academic researchers, graduate students, and industrial practitioners who are interested in the general areas of control performance monitoring, business performance benchmarking, control system or instrument problem diagnosis will find this book a useful reference.

Using process topology in plant-wide control loop performance assessment

This contribution describes how disturbances in a control system can be isolated and diagnosed automatically based on plant topology. In order to demonstrate this, a prototype software has been designed and implemented which, when given an electronic process schematic of a plant and results from a data-driven analysis, allows the user to pose queries about the plant and to find root causes of plant-wide disturbances. This hybrid system puts together two new technologies: the plant topology information written in XML according to the computer aided engineering exchange (CAEX) schema and the results of a signal analysis tool called plant-wide disturbance analysis (PDA). The isolation and diagnosis of the root causes of plant-wide disturbances is enhanced when process connectivity is considered alongside the results of data-driven analysis.