Chronicle knowledge-based multi-level response prediction for predictive control by forest models in process industry

TL;DR: This study proposes a hybrid predictive control method combining chronicle knowledge and data-driven models, leveraging intrinsic knowledge of controlled variables to improve predictive performance and accuracy in process industries.

Abstract: The control output response prediction is confirmed to be crucial for predictive control in process industries. The data-driven approaches that adopt system-independent predictors for output response predictions of multiple controlled variables are promising due to their easy implementation using available open-source models. However, the intrinsic knowledge among controlled variables is ignored, for it cannot be fed into the data-driven models directly, including control events, control antecedents, and temporal constraint information, which inevitably limits the predictive performance. This study proposes a hybrid control output response prediction method embedded with chronicle knowledge and a data-driven model for predictive control. It starts from the knowledge discovery and instance pool establishment by symbolic techniques. Control intrinsic knowledge extracted from the instance pool will be integrated into the modeling of forest model-based predictors, where the knowledge will be leveraged for predictor structure designing and temporal constraints estimation. In this way, tailored predictors with multi-level structures will be devised to capture the intrinsic correlation of controlled variables with different working levels. The control output response predictors will finally be introduced into the design of predictive controllers to make multi-output response predictions. The experimental results show the superiority of the proposed method over baselines in the prediction accuracy as well as the capability of set-point tracking and disturbance rejection.