Performance Assessment Of Control Systems Research Articles

Evaluation of Traffic Signal Systems Effectiveness in Connected Vehicle Environments Using Trajectory Analytics

One of the ways to design more effective signal control strategies is to leverage and synthesize connected vehicle generated (CVG) information to identify traffic states for the controller to operate in a predictive, yet vehicle-actuated manner. The contribution of this paper is twofold: (1) it presents a framework for an advanced, online, signal control logic in a connected environment that utilizes information from connected vehicles (CVs) to augment high-resolution controller and/or sensor data, and (2) it applies the trajectory analytics to compare the performance of the new controller schemes with CVG data and functionalities relative to conventional, vehicle-actuated, control. The framework puts forward a predictive control logic that schedules phases in an acyclic manner over a variable planning horizon. Phase duration is continually evaluated in response to updated requests for service distributed among equipped vehicles and associated performance indicators. Within the same connected control setup, two measures of effectiveness of a decision were compared to determine the upper bound on the potential effectiveness of a more responsive control strategy. Finally, trajectory analytics was used to evaluate the effectiveness of the CV technology-based control scheme against the conventional one. The findings indicate that both control system performance assessment and optimization objectives should change with access to CVG data. Unlike current state of the practice controllers, the developed method is able to handle high and low demand states equally well. The designed connected controller is shown to be robust in handling varying traffic conditions and demand levels.

Control performance monitoring and degradation recovery in automatic control systems: A review, some new results, and future perspectives

This paper addresses control performance monitoring (CPM) and degradation recovering in automatic control systems. It begins with a re-visit of CPM techniques and a summary of the major limitations of the existing CPM methods. They are (i) deficit in assessing control performance degradation caused by different types of disturbances and environment uncertainties, (ii) incapability for predicting performance degradation, and (iii) deficiency of efficient performance degradation recovering methods. In order to meet increasing demands of next generation automatic control systems for higher system performance, novel CPM methods have been developed in recent years, including performance assessment of control systems with deterministic disturbances and uncertainties, prediction of control performance degradation, and recovery of control performance degradation. Some of these methods and algorithms are introduced in the second part of this paper. The basis of these methods is a so-called residual centred model of feedback control systems, which allows a unified handling of control, monitoring and diagnosis in feedback control systems corrupted by disturbances and uncertainties. The focuses of these methods are on (i) introduction of the loop performance degradation index for the assessment and prediction of performance degradation in automatic control systems, (ii) predictive detection and estimation of loop performance degradation, and (iii) a data-driven performance degradation recovering scheme. The paper is concluded by a short summary of three future perspective topics, (i) prediction of economic system performance monitoring and estimation, (ii) reinforcement learning aided system performance recovery, and (iii) CPM digital twin.

Data-driven performance assessment of multivariable control loops using a modified Hurst exponent–based index

Control system performance assessment is significant, especially in practical applications. One of the most important indices for the performance assessment of control systems is minimum variance. Calculating the minimum variance index in multivariate systems requires prior knowledge of system parameters and models, and is therefore an obstacle in practical applications. In this article, an index is proposed for the performance assessment of multivariate control loops, evaluating the system performance with the minimum variance criterion and using only the system’s routine operation data. This index can quantify the performance using neither any prior knowledge of system parameters nor the system’s optimal operation data. The proposed index is based on the Hurst exponent, a parameter for measuring correlations in time series data. In this article, detrended fluctuation analysis and rescaled range analysis are used to estimate the Hurst exponents of system outputs. Using a combination of these Hurst exponents, an index is defined for the performance assessment of multivariate systems. The results of simulation examples illustrate that the proposed index can assess the performance efficiently.

Performance Assessment of Non-Gaussian Control Systems Based on Mixture Correntropy

The performance assessment of any control system plays a key role in industrial control systems. To meet the real-time requirements of modern control systems, a quick and accurate evaluation of the performance of a system is necessary. In this paper, a performance assessment method of a non-Gaussian control system based on mixture correntropy is proposed for non-Gaussian stochastic systems. Mixture correntropy can solve the problem of minimum entropy translation invariance. When the expected output of a system is unavailable, mixture correntropy combined with the estimation of distribution algorithm (EDA) is used for system identification and noise distribution estimation so as to calculate the benchmark of entropy-based performance assessment. When the expected output of a system is available, the mixture correntropy is directly used as the index to evaluate the performance of the system. To improve the real-time aspect of the performance assessment, an improved EDA is presented to obtain the evaluation index more quickly. For both Gaussian and non-Gaussian systems, the mixture correntropy and the improved identification algorithm are used for system performance assessment, and the results are compared with the minimum entropy index and the probability density function (PDF) curve coincident area index. The comparisons verify the rationality and effectiveness of the correntropy index and the rapidity of the improved EDA algorithm.

Generalized Multivariable Control System Performance Assessment Based on Output Variance Limit

Generalized Multivariable Control System Performance Assessment Based on Output Variance Limit

PCA−ARMA-Based Control Charts for Performance Monitoring of Multivariable Feedback Control

Assessing the effectiveness of control systems and limiting the incidence of fault events are main emphases of studies on performance assessment of control systems. In single-input/single-output (S...

Economic performance assessment of two ROM ore milling circuit controllers

Economic performance assessment of control systems is receiving increasing attention in both academia and industry. A relevant framework is proposed in Bauer and Craig (Bauer, M., Craig, I.K., 2008. Economic assessment of advanced process control – a survey and framework. Journal of Process Control 18 (1), 2–18) based on a comprehensive survey of the methods for economic analysis of advanced process control systems. The framework provides a formal procedure for the economic assessment of control systems. A practically motivated simulation study is used to cast the economic performance assessment of a nonlinear model predictive controller and three single-loop PID controllers for a run-of-mine milling circuit into this framework. Performance functions of the milling circuit, which dynamically relate the circuit process variables to monetary values, are developed based on a survey study on milling circuits and an in-depth literature study.

Performance assessment of control systems in rolling mills – application to strip thickness and flatness control

This contribution takes the control performance monitoring (CPM) technology into a new industrial area, the metal processing, where not much work has been done before. It is shown how to explore not yet discovered opportunities for controller performance improvement in this industrially interesting field, where special aspects have to be considered. This includes online vs. batch-wise performance evaluation, time-based vs. length-based assessment, and oscillation diagnosis. The paper presents two industrial case studies of the performance evaluation of control systems in a tandem cold rolling mill: (1) a feedforward/feedback strip thickness controller; and (2) an internal model control of the strip flatness. Routine operating data are analysed to obtain the performance monitoring metrics in terms of minimum variance index, oscillation index, and to suggest improvement measures. A monitoring tool for calculating and displaying the performance indices has been developed, and tailored to the applications in this field. The results indicate that tuning the feedback thickness controller is suggested to better handle entry thickness disturbances for specific coils. The performance of the flatness controller is found to be satisfactory, thus no actions are needed.

Performance Assessment of Plantwide Control Systems of Industrial Processes

Performance assessment of control systems has been receiving growing attention in the recent past to improve operability and profit margins of the process. However, such studies, and also the tools available to carry out such studies, from the plantwide perspective are rather limited. In this regard, a new dynamic performance index, called dynamic disturbance sensitivity (DDS) is proposed in this paper. It is then used to assess the performance of three plantwide control (PWC) structures (CS1, CS2, and CS3) for an industrially important petrochemical process, namely, the hydrodealkylation (HDA) of toluene to produce benzene. The three control structures are distinctly different from the throughput manipulator standpoint: CS1 uses an internal manipulator (e.g., reactor temperature), CS2 uses a fixed-feed control strategy, and CS3 uses an on-demand control strategy to control throughput; consequently, the rest of the control structure decisions are significantly different. By analyzing the results from rig...

Design and performance assessment of control systems using singular-value analysis

Cette methode est appliquee a un evaporateur double effet, en vue de concevoir et d'evaluer les schemas de commande a boucles multiples classiques avec des accroissements tels que des decoupleurs de boucle, des compensateurs de boucle de reaction positive et des boucles de commande internes

The Man-Machine System Concept

The man-machine concept as developed in this paper refers to a closed-loop control system comprising a machine and an actively participating human operator. The human component attributes are described with both general and specific engineering models. Particular emphasis is placed on the human adaptive characteristics which make for an unusual control system. Predictive behavior is discussed in terms of actual-physical and conceptual-effective display organizations resulting in compensatory, pursuit, and precognitive system organizations. Adaptive-operator transfer characteristics are presented, and applications of the adaptive model for random-input compensatory system design are listed. As an example, the model is used to estimate operator and system characteristics for a specific system, and the estirnates are compared with experimental results derived from associated empirical data. In a discussion of performance assessment, the basic attributes which any criterion for system performance should possess are reviewed, and a modus operandi for manual control system performance assessment is proposed.