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Data-driven multi-model control for a waste heat recovery system
2020
2020 59th IEEE Conference on Decision and Control (CDC)
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
doi:10.1109/cdc42340.2020.9304418
fatcat:olotltj76fgnfdzkteoyq5h3mq