Reachability analysis of closed-loop switching power converters

Hossain, Shamina; Dhople, Sairaj V.; Johnson, Taylor T.

doi:10.1109/peci.2013.6506047

Cited by 8 publications

(10 citation statements)

References 6 publications

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“…The reachability analysis framework has been introduced, and the proposed reachability analysis method is said to outperform the brute force Monte Carlo method in computation time and confidence level. Reachability analysis is also a research object in [57] for closed-loop switching power converters, and in [58] for power electronics dc-dc converters, aiming at reducing computation burden.…”

Section: Statistical Model Checkingmentioning

confidence: 99%

Overview of Control Algorithm Verification Methods in Power Electronics Systems

et al. 2021

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The paper presents the existing verification methods for control algorithms in power electronics systems, including the application of model checking techniques. In the industry, the most frequently used verification methods are simulations and experiments; however, they have to be performed manually and do not give a 100% confidence that the system will operate correctly in all situations. Here we show the recent advancements in verification and performance assessment of power electronics systems with the usage of formal methods. Symbolic model checking can be used to achieve a guarantee that the system satisfies user-defined requirements, while statistical model checking combines simulation and statistical methods to gain statistically valid results that predict the behavior with high confidence. Both methods can be applied automatically before physical realization of the power electronics systems, so that any errors, incorrect assumptions or unforeseen situations are detected as early as possible. An additional functionality of verification with the use of formal methods is to check the converter operation in terms of reliability in various system operating conditions. It is possible to verify the distribution and uniformity of occurrence in time of the number of transistor switching, transistor conduction times for various current levels, etc. The information obtained in this way can be used to optimize control algorithms in terms of reliability in power electronics. The article provides an overview of various verification methods with an emphasis on statistical model checking. The basic functionalities of the methods, their construction, and their properties are indicated.

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Section: Statistical Model Checkingmentioning

confidence: 99%

Overview of Control Algorithm Verification Methods in Power Electronics Systems

et al. 2021

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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%

“…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

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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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“…In future work, we intend to further investigate closed-loop control to address issues that arise in closed-loop linear controllers described in more depth in [3]. The basic issue is that the controller dynamics are significantly faster-an order of magnitude or more-than the plant dynamics, which leads to issues in determining appropriate parameters for the reachability analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Closed-loop challenges: In closed-loop, the duty cycle D is selected as a function of the output voltage V o . For typical closed-loop controllers (see, e.g., the closed-loop controllers in [3]), the controllers may be modeled as a linear system (ẋ = Ax), but dynamics of the controller are typically much faster than those of the plant (the circuit). Reachability analysis that relies on a uniform time step may struggle with either being too coarse (time advancement in too large of steps) or too slow (time advancement sufficiently small, but reachability analysis takes too long) [3].…”

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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Reachability analysis of closed-loop switching power converters

Cited by 8 publications

References 6 publications

Overview of Control Algorithm Verification Methods in Power Electronics Systems

Overview of Control Algorithm Verification Methods in Power Electronics Systems

Cyber-physical specification mismatch identification with dynamic analysis

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

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