Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers.
This paper presents review and comparison of alternative methodologies for control performance assessment. The approach uses nonlinear time series analysis, such as non-Gaussian statistics, fractal, crossover analysis, or entropy-based approaches. There is a presented practical rationale for the analysis. Evaluation is based on the real data gathered from industrial systems. Non-Gaussian analysis starts with statistical methods using different probabilistic distribution functions. As another potential measure, the Hurst exponent is calculated using different approaches. Finally, R/S plot analysis together with crossover point phenomenon discussion is presented. The paper ends with conclusions and presentation of open issues attractive for further development.
This
article presents final technical and economical results of
the comprehensive control rehabilitation project done for the ammonia
synthesis plant at JP Nawozy, GA ZAK S.A. The project included the
following activities: review of the existing control philosophies
and instrumentation infrastructure, design and implementation of the
new control loop templates, and tuning of the revised controls over
the entire installation. These activities improved installation operation
with much more reliable and repeatable dynamic responses. Simultaneously,
the overall installation efficiency has been also increased. This
initiative is considered as the prerequisite for further control system
upgrade with use of advanced process control and process optimization.
This article focuses on investigation of statistical approaches to the task of control performance assessment. Different statistical measures with Gaussian and non-Gaussian probabilistic distributions are taken into consideration. Analysis starts with the observations for simulated proportional-integral-derivative control error histograms followed by its statistical investigation using selected probabilistic distribution functions. Simulation experiments are followed by the analysis of control data originating from real industrial loops. Shadowing effect of long-tail control error histograms is identified, as it may significantly disable proper loop quality assessment. Results show that non-Gaussian approach with Cauchy or a-stable distributions seems to be reasonable assessment alternative in case of disturbances existing in industrial processes.
Model Predictive Control (MPC) is a well established advanced process control technology. There are many successful implementations of different predictive strategies in process industry. There may be found various modifications of the MPC, however, one aspect remains fixed. MPC performance index is in quadratic form. Nonetheless, statistical analysis frequently points out that the quadratic regression formulation has some drawbacks. It is sensitive against the outliers. This work analyzes alternative and robust formulations of the MPC embedded performance index. It is shown that the quadratic formulation is not an optimal one, while the linear 1 weight improves control. Classical 2 norm together with robust Cauchy and Dynamic Covariance Scaling gives worse results. INDEX TERMS MPC, performance index, robust regression, 1 norm, Dynamic Covariance Scaling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.