Integrating Deep Learning and Explicit MPC for Advanced Process Control

Abstract

For highly nonlinear systems, using deep learning models to capture complex dynamics is a promising feature for advanced control applications. Recently it has been shown that a particular class of deep learning models can be exactly recast in a mixed-integer linear programming formulation. Recasting a deep learning model as a set of piecewise linear functions enables the incorporation of advanced predictive models in model-based control strategies such as model predictive control. To alleviate the computational burden of solving the piecewise linear optimization problem online, multiparametric programming is utilized to obtain the full, offline, explicit solution of the optimal control problem. In this work, a strategy is presented for the integration of deep learning models, specifically neural networks with rectified linear units, and explicit model predictive control. The proposed strategy is demonstrated on the advanced control of a benchmark chemical process involving multiple reactors, a flash separator, and a recycle stream. The positive results showcase the relevance and strength of the proposed methodology.