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.

中文翻译：

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用于连续工厂的可证明安全的数字控制器的自动形式合成

我们提出了一种健全的自动化方法来为表示为时不变模型的物理工厂合成安全的数字控制器。模型是具有输入的线性微分方程，在连续状态空间上演化。综合精确地说明了控制器引入的有限精度算法的影响。该方法使用反例引导的归纳合成：归纳泛化阶段产生一个控制器，该控制器已知可以稳定模型，但对于模型的所有初始条件可能并不安全。然后通过有界模型检查来验证安全性：如果验证步骤失败，则向归纳概括提供反例，并且该过程进一步迭代直到获得安全控制器。