More Like this

1. Power Converter and Discrete Device Optimization Utilizing Discrete Time State-Space Modeling

   https://doi.org/10.1109/COMPEL52896.2023.10221030  

   Baxter, Jared A; Costinett, Daniel J (June 2023, 2023 IEEE 24th Workshop on Control and Modeling for Power Electronics (COMPEL))

   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. Converter Analysis Using Discrete Time State-Space Modeling

   https://doi.org/10.1109/COMPEL.2019.8769686  

   Baxter, Jared A.; Costinett, Daniel J. (June 2019, IEEE Workshop on Control and Modeling for Power Electronics (COMPEL))

   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
3. Steady-State Convergence of Discrete Time State-Space Modeling with State-Dependent Switching

   https://doi.org/10.1109/COMPEL49091.2020.9265734  

   Baxter, Jared A.; Costinett, Daniel J. (November 2020, 2020 IEEE 21st Workshop on Control and Modeling for Power Electronics (COMPEL))

   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. A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications

   https://doi.org/10.1115/1.4044524  

   Sharpe, Conner; Wiest, Tyler; Wang, Pingfeng; Seepersad, Carolyn Conner (December 2019, Journal of Mechanical Design)

   Abstract Supervised machine learning techniques have proven to be effective tools for engineering design exploration and optimization applications, in which they are especially useful for mapping promising or feasible regions of the design space. The design space mappings can be used to inform early-stage design exploration, provide reliability assessments, and aid convergence in multiobjective or multilevel problems that require collaborative design teams. However, the accuracy of the mappings can vary based on problem factors such as the number of design variables, presence of discrete variables, multimodality of the underlying response function, and amount of training data available. Additionally, there are several useful machine learning algorithms available, and each has its own set of algorithmic hyperparameters that significantly affect accuracy and computational expense. This work elucidates the use of machine learning for engineering design exploration and optimization problems by investigating the performance of popular classification algorithms on a variety of example engineering optimization problems. The results are synthesized into a set of observations to provide engineers with intuition for applying these techniques to their own problems in the future, as well as recommendations based on problem type to aid engineers in algorithm selection and utilization. 

   more » « less
5. MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models

   Imani, M; Ghoreishi, S.F.; Allaire, D.; Braga-Neto, U (July 2019, Proceedings of the ... AAAI Conference on Artificial Intelligence)

   Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems. Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well-known class of parameter estimation methods for this general class of state-space models. Existing SMC-based techniques rely on excessive sampling of the parameter space, which makes their computation intractable for large systems or tall data sets. Bayesian optimization techniques have been used for fast inference in state-space models with intractable likelihoods. These techniques aim to find the maximum of the likelihood function by sequential sampling of the parameter space through a single SMC approximator. Various SMC approximators with different fidelities and computational costs are often available for sample- based likelihood approximation. In this paper, we propose a multi-fidelity Bayesian optimization algorithm for the inference of general nonlinear state-space models (MFBO-SSM), which enables simultaneous sequential selection of parameters and approximators. The accuracy and speed of the algorithm are demonstrated by numerical experiments using synthetic gene expression data from a gene regulatory network model and real data from the VIX stock price index. 

   more » « less