Data-driven multi-model control for a waste heat recovery system

We consider the problem of supervised learning of a multi-model based controller for non-linear systems. Selected multiple linear controllers are used for different operating points and combined with a local weighting scheme, whose weights are predicted by a deep neural network trained online. The network uses process and model outputs to drive the controller towards a suitable mixture of operating points.The proposed approach, which combines machine learning and classical control of linear processes, consists in the design of a controller for a waste heat recovery system (WHRS) mounted on a Heavy-Duty (HD) truck engine to decrease fuel consumption and meet the future pollutant emissions standard.The proposed control scheme, which can be applied to any nonlinear system with an existing linear controller bank, is successfully evaluated on an Organic Rankine Cycle (ORC) process simulator and compared to a standard linear controller and to several strong multi-model baselines without learning.