Deep Learning-Based Inverse Modeling for Predictive Control

Deep learning architectures can accurately model complex dynamics such as Inverse models of nonlinear systems. Their uses in the control context have been well-developed in the past forming a strong basis. However, the black-box nature of such techniques have limited its usability in real- world applications. This work aims to provide an auxiliary framework that combines intelligent data-based information with the conventional Predictive Control approach. By adding the inverse model signals to the closed-loop optimization search, the performance and accuracy are improved in our test cases. We provide a set of benchmark experiments as well as different optimization algorithms that benefit from Inverse models. Our results show a clear addition of Intelligent mechanisms to the standard methodology, without the uncertainties of black-box models and with all the advantages.