Design verification methods for switching power converters

Johnson, Taylor T.; Hong, Zhihao; Kapoor, Ashish

doi:10.1109/peci.2012.6184587

Cited by 11 publications

(10 citation statements)

References 31 publications

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“…We will formalize specifications and mismatches in Section 4. As a prelude, we highlight that Hynger finds this candidate invariant (that can be shown to be an actual invariant when modeled as a hybrid automaton [10,12,13]). …”

Section: Buck Converter With Hystere-sis Controlmentioning

confidence: 99%

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Cyber-physical specification mismatch identification with dynamic analysis

Johnson

Bak

Drager

2015

Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems

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Embedded systems use increasingly complex software and are evolving into cyber-physical systems (CPS) with sophisticated interaction and coupling between physical and computational processes. Many CPS operate in safety-critical environments and have stringent certification, reliability, and correctness requirements. These systems undergo changes throughout their lifetimes, where either the software or physical hardware is updated in subsequent design iterations. One source of failure in safety-critical CPS is when there are unstated assumptions in either the physical or cyber parts of the system, and new components do not match those assumptions. In this work, we present an automated method towards identifying unstated assumptions in CPS. Dynamic specifications in the form of candidate invariants of both the software and physical components are identified using dynamic analysis (executing and/or simulating the system implementation or model thereof). A prototype tool called Hynger (for HYbrid iNvariant GEneratoR) was developed that instruments Simulink/Stateflow (SLSF) model diagrams to generate traces in the input format compatible with the Daikon invariant inference tool, which has been extensively applied to software systems. Hynger, in conjunction with Daikon, is able to detect candidate invariants of several CPS case studies. We use the running example of a DC-to-DC power converter, and demonstrate that Hynger can detect a specification mismatch where a tolerance assumed by the software is violated due to a plant change.

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Section: Buck Converter With Hystere-sis Controlmentioning

confidence: 99%

“…The buck converter plant may be modeled as a hybrid automaton as described in detail [10][11][12][13].…”

Section: Buck Converter Plant Modelmentioning

confidence: 99%

Cyber-physical specification mismatch identification with dynamic analysis

Johnson

Bak

Drager

2015

Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems

Self Cite

View full text Add to dashboard Cite

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“…LGG algorithm of open-loop buck converter output voltage using parameters from Table 1. The red and blue sets overapproximate different parameter extrema [4]. 2.…”

Section: Switching Frequencymentioning

confidence: 99%

Benchmark: DC-to-DC Switched-Mode Power Converters (Buck Converters, Boost Converters, and Buck-Boost Converters)

Nguyen¹,

Johnson²

EPiC Series in Computing

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Power electronics represent a large and important class of hybrid systems, as modern digital computers and many other systems rely on their correct operation. In this benchmark description, we model three DC-to-DC switched-mode power converters as hybrid automata with continuous dynamics specified by linear ordinary differential equations. A DC-to-DC converter transforms a DC source voltage from one voltage level to another utilizing switches toggled at some (typically kilohertz) frequency with some duty cycle. The state of this switch gives rise to the locations of the hybrid automaton, and the continuous variables are currents and voltages. The main contributions of this benchmark description include: (a) unified modeling of three types of converters as a hybrid automaton with two locations and differing continuous dynamics, and (b) a basic benchmark generator that allows for simulation of these converters in Simulink/Stateflow and reachability analysis in SpaceEx. Future challenges for these benchmark classes include closed-loop control, where the speeds of plant and controller dynamics differ by orders of magnitude.

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“…Verification and validation are important tasks that are applied broadly in many fields in recent years such as embedded systems, power electronics, networked control systems, and aerospace systems [4,16,17]. Many different verification methods and tools have been developed for reachability analysis of hybrid systems [2,3,7,14].…”

Section: Context and Originsmentioning

confidence: 99%

Benchmark: A Nonlinear Reachability Analysis Test Set from Numerical Analysis

Tran¹,

Nguyen²,

Johnson³

EPiC Series in Computing

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The field of numerical analysis has developed numerous benchmarks for evaluating differential and algebraic equation solvers. In this paper, we describe a set of benchmarks commonly used in numerical analysis that may also be effective for evaluating continuous and hybrid systems reachability and verification methods. Many of these examples are challenging and have highly nonlinear differential equations and upwards of tens of dimensions (state variables). Additionally, many examples in numerical analysis are originally encoded as differential algebraic equations (DAEs) with index greater than one or as implicit differential equations (IDEs), which are challenging to model as hybrid automata. We present executable models for ten benchmarks from a test set for initial value problems (IVPs) in the SpaceEx format (allowing for nonlinear equations instead of restricting to affine) and illustrate their conversion to several other formats (dReach, Flow*, and the MathWorks Simulink/Stateflow [SLSF]) using the HyST tool. For some instances, we present successful analysis results using dReach, Flow*, and SLSF. Category: academic Difficulty: low through challenge Context and OriginsVerification and validation are important tasks that are applied broadly in many fields in recent years such as embedded systems, power electronics, networked control systems, and aerospace systems [4,16,17]. Many different verification methods and tools have been developed for reachability analysis of hybrid systems [2,3,7,14]. The challenges in verification of continuous and hybrid systems are many, and include for example complex nonlinear dynamics, highdimensional state-spaces, and bounded vs. unbounded time. To evaluate novel verification methods and tools, we need to evaluate and test them using a variety of diverse benchmarks, that are ideally standardized. However, these benchmarks are not standardized, so it is difficult to evaluate whether particular state representations (e.g., zonotopes [1], Taylor models [7], support functions [13], polyhedra, hypercubes [5], symbolic/SMT formulas [14], etc.) and verification techniques are superior for different classes of hybrid automata.In this paper, we present a set of ten different, executable benchmarks to aid in the development of a standardized set of benchmarks for the verification community to evaluate verification methods and tools. These benchmarks are derived from a test set for initial value problem (IVP) G.Frehse and M

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Design verification methods for switching power converters

Cited by 11 publications

References 31 publications

Cyber-physical specification mismatch identification with dynamic analysis

Cyber-physical specification mismatch identification with dynamic analysis

Benchmark: DC-to-DC Switched-Mode Power Converters (Buck Converters, Boost Converters, and Buck-Boost Converters)

Benchmark: A Nonlinear Reachability Analysis Test Set from Numerical Analysis

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