混合系统的分散集值状态估计与预测：一种符号方法
Decentralized set-valued state estimation and prediction for hybrid systems: A symbolic approach
AI总结：
提出一种符号方法，通过分解外部信号空间将混合系统的状态估计与预测任务分散到多个分布式状态机，以降低计算复杂度，并利用转移关系的结构性质恢复精确状态集。
摘要：
针对具有混合状态机表示的系统，提出了一种分散集值状态估计与预测的符号方法。分散计算方案表示有限个分布式状态机的合取，这些状态机通过外部信号空间的适当分解来指定。其目标是将计算任务分解为更小的子任务，分配给各个分布式状态机，从而可能显著降低整体空间/时间计算复杂度。我们证明，通常这种方案外逼近原始整体状态机的状态集估计和预测。下一步，通过利用后者转移关系的结构性质，我们提出了用于恢复精确状态集结果的构造性分解算法。
英文摘要：
A symbolic approach to decentralized set-valued state estimation and prediction for systems that admit a hybrid state machine representations is proposed. The decentralized computational scheme represents a conj unction of a finite number of distributed state machines, which are specified by an appropriate decomposition of the external signal space. It aims at a distribution of computational tasks into smaller ones, allocated to individual distributed state machines, leading to a potentially significant reduction in the overall space/time computational complexity. We show that, in general, such a scheme outerapproximates the state set estimates and predictions of the original monolithic state machine. By utilizing structural properties of the transition relation of the latter, in a next step, we propose constructive decomposition algorithms for a recovery of the exact state set outcomes.
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