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Ensuring safety for sampled data systems: An efficient algorithm for filtering potentially unsafe input signals

机译:确保采样数据系统的安全性:一种有效的算法,用于过滤可能不安全的输入信号

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A common design pattern in cyber-physical systems features a continuous plant and a discrete controller in a feedback loop. Sampled data analysis attempts to take into consideration both the continuous and discrete time elements of such a design. In this paper we adapt an earlier algorithm for efficient ellipsoidal approximation of robust sampled data finite horizon viability kernels to compute capture basins for systems with linear dynamics. Using these capture basins, we construct a hybrid automaton which can verify and if necessary modify an exogenous input signal to ensure safety. The hybrid automaton can be run online in the controller so that it can handle exogenous input signals arriving in real time, such as might be generated by human-in-the-loop control. The technique is demonstrated on a six dimensional nonlinear longitudinal model of a quadrotor with a human pilot in the loop. The capture basins' robustness is used to handle the model nonlinearity in a sound fashion.
机译:网络物理系统中的常见设计模式在反馈回路中具有连续的工厂和离散的控制器。采样数据分析试图同时考虑这种设计的连续和离散时间元素。在本文中,我们采用了一种较早的算法,用于对鲁棒采样数据有限层数生存力内核进行有效的椭圆近似,以计算具有线性动力学系统的捕获盆地。使用这些捕获池,我们构建了一个混合自动机,该自动机可以验证并在必要时修改外来输入信号以确保安全。混合自动机可以在控制器中在线运行,以便它可以处理实时到达的外来输入信号,例如人在回路控制中可能产生的信号。该技术在具有人工驾驶员的四旋翼飞机的六维非线性纵向模型中得到了证明。捕获盆地的鲁棒性用于以合理的方式处理模型非线性。

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