skip to main content


Title: Converter Analysis Using Discrete Time State-Space Modeling
Modeling plays a vital role in the design of advanced power converters. Commonly, modeling is completed using either dedicated hand analysis, which must be completed individually for each topology, or time-stepping circuit simulations, which are insufficiently rapid for broad analysis considering a wide range of potential designs or operating points. Discrete time state-space modeling of switching converters has shown merits in rapid analysis and generality to arbitrary circuit topologies but is hampered by difficulty incorporating nonlinear elements. In this work, we investigate methods for the incorporation of nonlinear elements into a generalized discrete time state-space modeling framework and showcase the utility of the approach for use in the converter design process.  more » « less
Award ID(s):
1751878
NSF-PAR ID:
10132884
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE Workshop on Control and Modeling for Power Electronics (COMPEL)
Page Range / eLocation ID:
1 to 8
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Broad-scale modeling and optimization play a vital role in the design of advanced power converters. Optimization is normally implemented via brute force iterations of design variables or utilizing metaheuristic techniques which are time consuming for a wide range of potential topologies, device implementations, and operating points. Recently, discrete time state-space modeling has shown merits in rapid analysis and generality to arbitrary circuit topologies but has not yet been utilized under rapid optimization techniques across multiple converter parameters. In this work, we investigate methods to incorporate rapid gradient-based optimization techniques to leverage discrete time state-space modeling and showcase the approach in the power converter design process. The method is validated on a 48-to-1V converter designed using the proposed techniques. 
    more » « less
  2. Schematic-level optimization and steady-state loss modeling play a vital role in the design of advanced power converters. Recently, discrete time state-space modeling has shown merits in rapid analysis and generality to arbitrary circuit topologies but has not yet been utilized under rapid optimization techniques. In this work, we investigate methods for the incorporation of rapid gradient-based optimization techniques leveraging discrete time state-space modeling and showcase the utility of the approach for use in the converter design process. 
    more » « less
  3. null (Ed.)
    Steady-state modeling plays an important role in the design of advanced power converters. Typically, steady-state modeling is completed by time-stepping simulators, which may be slow to converge to steady-state, or by dedicated analysis, which is time-consuming to develop across multiple topologies. Discrete time state-space modeling is a uniform approach to rapidly simulate arbitrary power converter designs. However, the approach requires modification to capture state-dependent switching, such as diode switching or current programmed modulation. This work provides a framework to identify and correct state-dependent switching within discrete time state-space modeling and shows the utility of the proposed method within the power converter design process. 
    more » « less
  4. This work explores the mechanisms and limitations of natural voltage balancing in flying capacitor multilevel (FCML) DC-DC converters. A simple discrete-time state space model is used to explore the fundamental conditions that will lead to (or prevent) natural balance of flying capacitor voltages, along with the balancing dynamics. The treatment is used to highlight straightforward ways to alleviate problems with natural imbalance by adjusting the switching scheme. The model is compared against circuit simulations and the proposed switching scheme is verified in a hardware prototype. 
    more » « less
  5. Abstract

    Output reference tracking can be improved by iteratively learning from past data to inform the design of feedforward control inputs for subsequent tracking attempts. This process is called iterative learning control (ILC). This article develops a method to apply ILC to systems with nonlinear discrete‐time dynamical models with unstable inverses (i.e., discrete‐time nonlinear nonminimum phase models). This class of systems includes piezoactuators, electric power converters, and manipulators with flexible links, which may be found in nanopositioning stages, rolling mills, and robotic arms, respectively. As these devices may be required to execute fine transient reference tracking tasks repetitively in contexts such as manufacturing, they may benefit from ILC. Specifically, this article facilitates ILC of such systems by presenting a new ILC synthesis framework that allows combination of the principles of Newton's root finding algorithm with stable inversion, a technique for generating stable trajectories from unstable models. The new framework, called invert‐linearize ILC (ILILC), is validated in simulation on a cart‐and‐pendulum system with model error, process noise, and measurement noise. Where preexisting Newton‐based ILC diverges, ILILC with stable inversion converges, and does so in less than one third the number of trials necessary for the convergence of a gradient‐descent‐based ILC technique used as a benchmark.

     
    more » « less