Use of a Partially Saturating Inductor in a Boost Converter with Model Predictive Control
Pietro Firpo, Alessandro Ravera, Alberto Oliveri, Matteo Lodi, Marco Storace
Electronics
Abstract
Increasing the power density in switched mode power supplies is one of the main goals in power electronics. This aim can be achieved by using smaller inductors operating at partial magnetic saturation. In this work, a partially saturating ferrite core inductor is exploited in a switching DC-DC boost converter, regulated through nonlinear model predictive control. A nonlinear behavioral inductor model, identified through experimental measurements, accounts for both magnetic saturation and losses. The simulation results show that the converter output voltage is correctly regulated and the imposed current constraints are fulfilled, even when partial magnetic saturation occurs. Comparisons with traditional control techniques are also presented.
Co-Design of a Controller and Its Digital Implementation: The MOBY-DIC2 Toolbox for Embedded Model Predictive Control
Alessandro Ravera, Alberto Oliveri, Matteo Lodi, Alberto Bemporad, W.P.M.H. Heemels, Eric C. Kerrigan, Marco Storace
IEEE Transactions on Control Systems Technology
Abstract
Several software tools are available in the literature for the design and embedded implementation of linear model predictive control (MPC), both in its implicit and explicit (either exact or approximate) forms. Most of them generate C code for easy implementation on a microcontroller, and the others can convert the C code into hardware description language code for implementation on a field programmable gate array (FPGA). However, a unified tool allowing one to generate efficient embedded MPC for an FPGA, starting from the definition of the plant and its constraints, was still missing. The MOBY-DIC2 toolbox described in this brief bridges this gap. To illustrate its functionalities, the tool is exploited to embed the controller and observer for a real buck power converter in an FPGA. This implementation achieves a latency of about 30
Supplementary material
Code available on Code Ocean.
Nonlinear models of power inductors: a survey
Alberto Oliveri, Matteo Lodi, Marco Storace
International Journal on Circuit Theory and Applications
Abstract
Switch-mode power supplies (SMPSs) are widely exploited to interface electrical energy sources to motors and other electrical loads. Inductors are usually the biggest and heaviest components in SMPSs, limiting their overall power density. Therefore, there is an increasing interest in designing SMPSs with partially saturating inductors, because this significantly reduces their weight and size, thus increasing power density. This paper provides a review of nonlinear behavioral models (based on easy-to-measure quantities) of the inductance, power loss, and temperature rise of inductors working, at least partially, in magnetic saturation. This survey discusses the pros, cons, and ranges of validity of these models, with a glimpse at their application to SMPS simulation, design, monitoring, and control.
One-way dependent clusters and stability of cluster synchronization in directed networks
Matteo Lodi, Francesco Sorrentino, Marco Storace
Nature Communications vol. 12, n. 4073 (2021)
Abstract
Cluster synchronization in networks of coupled oscillators is the subject of broad interest from the scientific community, with applications ranging from neural to social and animal networks and technological systems. Most of these networks are directed, with flows of information or energy that propagate unidirectionally from given nodes to other nodes. Nevertheless, most of the work on cluster synchronization has focused on undirected networks. Here we characterize cluster synchronization in general directed networks. Our first observation is that, in directed networks, a cluster A of nodes might be one-way dependent on another cluster B: in this case, A may remain synchronized provided that B is stable, but the opposite does not hold. The main contribution of this paper is a method to transform the cluster stability problem in an irreducible form. In this way, we decompose the original problem into subproblems of the lowest dimension, which allows us to immediately detect inter-dependencies among clusters. We apply our analysis to two examples of interest, a human network of violin players executing a musical piece for which directed interactions may be either activated or deactivated by the musicians, and a multilayer neural network with directed layer-to-layer connections.