Multivariate Control Loop Performance Assessment With Hurst Exponent and Mahalanobis Distance


A novel knowledge-driven technique for performance assessment of multivariate control loops that takes under consideration the interactions at intervals the system is proposed. The technique merges the Hurst-exponent-based single-input single-output controller performance index with Mahalanobis distance to devise a multiple-input multiple-output (MIMO) controller performance index. The distinct advantage over the quality minimum variance index and novelty of the proposed approach lies in its ability to quantify the performance of MIMO controller without the information of interactor matrix or system description, that ends up in the technique being insensitive to model plant mismatch and easily applicable to nonlinear systems. Solely closed-loop routine operating data are needed. This new methodology is tested on benchmark systems from the literature and simulation results are presented. Comparison with minimum variance index-based mostly techniques reveals wonderful agreement within the trends of both approaches. The results establish the proposed approach as a promising tool for interactor-matrix-freelance MIMO control loop performance assessment.

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