Performance Assessment of MIMO System With Non-Gaussian Disturbances

Performance assessment of control loops is of great importance for industrial production. This paper proposes a novel performance assessment and controller tuning method for non-Gaussian MIMO feedback control systems. First, an algorithm based on minimum entropy and mutual information projection to latent structure (ME-PLS) was proposed to replace the canonical correlation analysis algorithm (CCA). The ME-PLS algorithm decomposes the system data into independent components related to inputs and outputs, and this algorithm applies to both Gaussian and non-Gaussian systems. Each pair of principal components represents a virtual non-Gaussian control loop. Next, the performance of each virtual loop is calculated separately with the non-Gaussian minimum entropy method. Finally, to identify the parameters of each virtual loop, the author gives a least absolute deviation iterative algorithm based on the CARMA model (CARMA-LADI). When the control does not work well, based on the ME-PLS algorithm's relationship and the CARMA-LADI algorithm's identification results can give the controller tuning direction.