A probabilistic framework with concurrent analytics of Gaussian process regression and classification for multivariate control performance assessment

Abstract

Abstract Control performance assessment (CPA) is vital to ensure the safety of control systems. However, most multivariate CPA methods are limited to the system with explicit knowledge. Recently, it has been recognized that high predictability of closed-loop outputs implies poor control performance. This paper proposes a probabilistic CPA (PCPA), which is compatible with the above promising idea. This paper constructs a modified Gaussian process regression (GPR) model to quantitatively\u200b estimate the prediction uncertainty of outputs with only routine closed-loop data as input and focuses on the prediction variance of interest. As a non-parametric and probabilistic method, the proposed framework can handle the nonlinearity and random uncertainties inherent in complex control systems. Combined with the varying window size strategy, a novel performance metric, called disruption resistance (DR) here, is designed to characterize different control performance. The evaluation confidence and uncertainty can be revealed with concurrent analytics of GPR and Gaussian process classification when performing the performance assessment. This gives rise to a reliable and pragmatic PCPA framework, which shows more accurate and comprehensive results in the application to both simulated and real industrial processes.