Practical control performance assessment method using Hurst exponents and crossover phenomena

This paper proposes a new Hurst exponent-based method to evaluate the performance of control loops by estimating Minimum Variance Index (MVI). Incorrect estimation of time delays dramatically affects MVI-based performance assessment accuracy. The main advantage of the proposed method over existing methods is that it only requires routine operation data, without any need for prior information of the system model parameters and time delays. Hence, this method is more suitable to be applied on various industrial control processes. Recently, crossover phenomena have been reported in the graphs used to estimate the Hurst exponent of different control signals, which complicate deciding the Hurst exponents values. To address this problem, the crossover phenomenon is investigated for control signals, then the Hurst index is modified based on it. The proposed method is successfully used to assess performance of various types of control loops such as the cascade loop and verified through simulation results. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a new method based on the Hurst exponent for evaluating the performance of control loops by estimating the Minimum Variance Index (MVI). The method only requires routine operation data, making it suitable for various industrial control processes.