Powertrain control verification benchmark

Jin, Xiaoqing; Deshmukh, Jyotirmoy V.; Kapinski, James; Ueda, Koichi; Butts, Ken

doi:10.1145/2562059.2562140

Cited by 158 publications

(156 citation statements)

References 33 publications

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“…The model we present is a closedloop model, i.e., it contains a model of the plant and a model of the controller. This model is unchanged from the one that appeared before in [7] and [8].…”

Section: A/f Ratio Control Modelmentioning

confidence: 96%

Piecewise-Affine Approximations for a Powertrain Control Verification Benchmark

Deshmukh¹,

Ito²,

Jin³

et al.

EPiC Series in Computing

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We present a benchmark example of an automotive powertrain control system converted to a hybrid system with piecewise-affine (PWA) continuous dynamics. The purpose is to provide an example of an industrial nonlinear system that is amenable to existing software tools for performing verification of safety properties for hybrid systems. Existing algorithmic approaches to hybrid system verification require that system representations are restricted to specific classes of models. Therefore, it is important to develop and evaluate techniques to generate approximate models that adhere to the required class of systems without introducing an unacceptable amount of approximation errors. The example we present is intended to demonstrate a symbolic method to automatically approximate a nonlinear model with a PWA approximation while respecting a given bound on the approximation error. While existing tools are applicable to the resulting model, the scale of the PWA model is intended to challenge the capabilities of these tools. We conduct an experimental comparison between the original and PWA models and present our observations and discuss challenges for the research community.

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“…The model we present is a closedloop model, i.e., it contains a model of the plant and a model of the controller. This model is unchanged from the one that appeared before in [7] and [8].…”

Section: A/f Ratio Control Modelmentioning

confidence: 96%

Piecewise-Affine Approximations for a Powertrain Control Verification Benchmark

Deshmukh¹,

Ito²,

Jin³

et al.

EPiC Series in Computing

Self Cite

View full text Add to dashboard Cite

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“…The benchmark suite of powertrain control systems were published in [11,10] as challenge problems for hybrid system verification. The suite has a set of Simulink TM models with increasing levels of sophistication and fidelity.…”

Section: The Powertrain Benchmarksmentioning

confidence: 99%

“…Breach [2] and STaliro [1] have been used for finding counterexamples (or falsifying) models in [13,11,12,3]. Our main verification results for this benchmark have been reported in [4].…”

Section: The Powertrain Benchmarksmentioning

confidence: 99%

“…Models 2 and 3 of [11] translate to hybrid automata with 4 locations and 5 continuous variables. The locations are startup, normal, power, and sensor fail.…”

Section: Model Transformationmentioning

confidence: 99%

“…Model 2 (the second model in [11]) differs in two aspects: (1) the right-hand side of the system equations are general nonlinear functions instead of polynomial functions; (2) only two of the four variables are continuous, other two are discrete variables updated periodically.…”

Section: Model Transformationmentioning

confidence: 99%

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Progress on Powertrain Verification Challenge with C2E2

Fan

Duggirala

Mitra

et al.

EPiC Series in Computing

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In this paper, we present the progress we have made in verifying a benchmark powertrain control system. We implemented the on-the-fly algorithm for computing discrepancy of nonlinear dynamical systems in the C2E2 verification tool. We created Stateflow translations of the original models to aid the processing using C2E2 tool and we encoded the different driver behaviors in the form of state machines. With these customizations, we have been successful in verifying one of the benchmarks from the powertrain suite. In this paper, we discuss the engineering challenges and the lessons learned from the process. The powertrain benchmarksThe benchmark suite of powertrain control systems were published in [11,10] as challenge problems for hybrid system verification. The suite has a set of Simulink TM models with increasing levels of sophistication and fidelity. At a high-level, all the models take inputs from a driver (throttle angle) and the environment (sensor failures), and define the dynamics of the engine. The key controlled quantity is the air to fuel ratio which in turn influences the emissions, the fuel efficiency, and torque generated.The first model (model 1) is the most complex. It has look-up tables, delayed differential equations, and switches. Models 2 and 3 are simpler but still complicated enough for most hybrid verifcation tools. Model 3 is a hybrid automaton with polynomial differential equations and continuously computed control inputs, and Model 2 is similar but with nonlinear differential equations and both continuous and discretely sampled variables. The requirements for the system are stated in signal temporal logic (STL). A typical property, for example, 3 t (x ∈ [x eq − , x eq + ]), states that after t units of time, the continuous variable x is within the range x eq ± . * We thank Jim Kapinski, Jyotirmoy Deshmukh, and Xiaoqing Jin of Toyota for several useful discussions on the powertrain models.

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