Abstract
Performance monitoring of model predictive control systems (MPC) has received a great interest from both academia and industry. In recent years some novel approaches for multivariate control performance monitoring have been developed without the requirement of process models or interactor matrices. Among them the prediction error approach has been shown to be a promising one, but it is k-step prediction based and may not be fully comparable with the MPC objective that is multi-step prediction based. This paper develops a multi-step prediction error approach for performance monitoring of model predictive control systems, and demonstrates its application in an industrial MPC performance monitoring and diagnosis problem.
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