Evaluation-based closed-loop errors using principal component analysis and self-organisation map with an application to a pickling process

Closed-loop control is a basic technology in control engineering. Its role is to avoid the tracking error between set points and real variables. The evaluation of plant performance can be based on multivariate statistical process control connected to closed-loop errors behaviour. Due to its practicality, this approach has found many applications in several industries. This paper suggests a combined use of principal component analysis (PCA) and self-organisation map (SOM) algorithms to evaluate the process on the basis of closed-loop errors dynamic. Generally, it is possible to evaluate a product quality in the basis of the dynamic changes of the closed-loop control errors. In this paper, a new method based on the analysis of the control errors is proposed; it is carried out by a combined use of the PCA-SOM algorithm. Comparatively to the conventional PCA method, this new technique is characterised by the performant indexes that give an accurate evaluation of the process variability and its impact on the product quality. As shown in the different simulation results, the proposed approach gives a global evaluation and improves considerably the performance of computed indexes used for the evaluation of the controlled process.