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Automated formal synthesis of provably safe digital controllers for continuous plants

We present a sound and automated approach to synthesizing safe, digital controllers for physical plants represented as time-invariant models. Models are linear differential equations with inputs, evolving over a continuous state space. The synthesis precisely accounts for the effects of finite-preci...

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Detalles Bibliográficos
Autores principales: Abate, Alessandro, Bessa, Iury, Cordeiro, Lucas, David, Cristina, Kesseli, Pascal, Kroening, Daniel, Polgreen, Elizabeth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7056743/
https://www.ncbi.nlm.nih.gov/pubmed/32189718
http://dx.doi.org/10.1007/s00236-019-00359-1
Descripción
Sumario:We present a sound and automated approach to synthesizing safe, digital controllers for physical plants represented as time-invariant models. Models are linear differential equations with inputs, evolving over a continuous state space. The synthesis precisely accounts for the effects of finite-precision arithmetic introduced by the controller. The approach uses counterexample-guided inductive synthesis: an inductive generalization phase produces a controller that is known to stabilize the model but that may not be safe for all initial conditions of the model. Safety is then verified via bounded model checking: if the verification step fails, a counterexample is provided to the inductive generalization, and the process further iterates until a safe controller is obtained. We demonstrate the practical value of this approach by automatically synthesizing safe controllers for physical plant models from the digital control literature.