Sergio Lucia — Page 3

arXiv2020

Deep Learning-Based Model Predictive Control for Resonant Power Converters

Resonant power converters offer improved levels of efficiency and power density. In order to implement such systems, advanced control techniques are required to take the most of the power converter. In this context, model predictive control arises as a powerful tool that is able to consider nonlinearities and constraints, but it requires the solution of complex optimization problems or strong simplifying assumptions that hinder its application in real situations. Motivated by recent theoretical advances in the field of deep learning, this paper proposes to learn, offline, the optimal control policy defined by a complex model predictive formulation using deep neural networks so that the online use of the learned controller requires only the evaluation of a neural network. The obtained learned controller can be executed very rapidly on embedded hardware. We show the potential of the presented approach on a Hardware-in-the-Loop setup of an FPGA-controlled resonant power converter.

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arXiv2017

Modeling metabolic networks including gene expression and uncertainties

Constraint based methods, such as the Flux Balance Analysis, are widely used to model cellular growth processes without relying on extensive information on the regulatory features. The regulation is instead substituted by an optimization problem usually aiming at maximal biomass accumulation. A recent extension to these methods called the dynamic enzyme-cost Flux Balance Analysis (deFBA) is a fully dynamic modeling method allowing for the prediction of necessary enzyme levels under changing environmental conditions. However, this method was designed for deterministic settings in which all dynamics, parameters, etc. are exactly known. In this work, we present a theoretical framework extending the deFBA to handle uncertainties and provide a robust solution. We use the ideas from multi-stage nonlinear Model Predictive Control (MPC) and its feature to represent the evolution of uncertainties by an exponentially growing scenario tree. While this representation is able to construct a deterministic optimization problem in the presence of uncertainties, the computational cost also increases exponentially. We counter this by using a receding prediction horizon and reshape the standard deFBA to the short-time deFBA (sdeFBA). This leads us, along with further simplification of the scenario tree, to the robust deFBA (rdeFBA). This framework is capable of handling the uncertainties in the model itself as well as uncertainties experienced by the modeled system. We applied these algorithms to two case-studies: a minimal enzymatic nutrient uptake network, and the abstraction of the core metabolic process in bacteria.

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