Model Predictive Real-Time Monitoring of Linear Systems
Model Predictive Real-Time Monitoring of Linear Systems
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DOI：
10.1109/rtss.2017.00035
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Xin Chen;S. Sankaranarayanan
中科院分区：
文献类型：
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作者：
Xin Chen;S. Sankaranarayanan
预测性监控问题询问在某些环境条件下，部署的系统是否有可能在接下来的T秒内发生故障。这个问题对网络物理系统至关重要，并激发了能够在预警时适应此类故障的实时架构。本文提出了一种线性模型预测方案，用于实时监测由时间触发控制器和时变扰动控制的线性系统。该方案使用离线（提前）和在线计算相结合的方法来确定给定的植物模型是否进入了一种状态，无论采用何种控制，干扰都有将系统驱动到不安全区域的策略。我们的方法独立于所使用的控制策略：这使我们能够处理使用模型预测控制技术或甚至不透明的基于机器学习的控制算法控制的植物，这些控制算法很难使用现有的可达集估计算法进行推理。我们的在线计算重用脱机计算的符号可达集。实时监视器使用具体的状态估计实例化可达集，并重复执行关于安全属性的空性检查。我们根据它们对整个系统的暗示对我们的方法提出的各种警报进行分类。我们在许多线性系统基准测试中实现了我们的实时监控方法，并表明在实践中计算可以快速执行。此外，我们还检查了我们的方法报告的警报，并展示了如何使用一些警报来改进控制器。
The predictive monitoring problem asks whether a deployed system is likely to fail over the next T seconds under some environmental conditions. This problem is of the utmost importance for cyber-physical systems, and has inspired real-time architectures capable of adapting to such failures upon forewarning. In this paper, we present a linear model-predictive scheme for the real-time monitoring of linear systems governed by time-triggered controllers and time-varying disturbances. The scheme uses a combination of offline (advance) and online computations to decide if a given plant model has entered a state from which no matter what control is applied, the disturbance has a strategy to drive the system to an unsafe region. Our approach is independent of the control strategy used: this allows us to deal with plants that are controlled using model-predictive control techniques or even opaque machine-learning based control algorithms that are hard to reason with using existing reachable set estimation algorithms. Our online computation reuses the symbolic reachable sets computed offline. The real-time monitor instantiates the reachable set with a concrete state estimate, and repeatedly performs emptiness checks with respect to a safety property. We classify the various alarms raised by our approach in terms of what they imply about the system as a whole. We implement our real-time monitoring approach over numerous linear system benchmarks and show that the computation can be performed rapidly in practice. Furthermore, we also examine the alarms reported by our approach and show how some of the alarms can be used to improve the controller.