Optimal sampling and switching policies for linear stochastic systems

Event-triggered sampling;Optimal stopping

Nar, Kamil ; Basar ; Tamer

关键词: Event-triggered sampling;    Optimal stopping;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/50571/Kamil_Nar.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

For systems with limited capacity for storage, processing and transmission of data, the choice of sampling policy is critical. Although most systems determine their sampling instants in advance, for example periodically, this results in unnecessary use of samples if little changes occur between sampling times. Instead, to optimize the utilization of the samples, the decision to take a sample can be adaptively made based on the importance of the change in the state of the system. This calls for development of event-triggered sampling policies. In this thesis, we study the optimal event-triggered sampling policies under a constraint on the frequency of sampling. We first investigate the optimal sampling policies to minimize the estimation error over the infinite horizon. The optimal policies are provided for multidimensional Wiener processes and scalar linear diffusion processes. Then, we address an infinite horizon control problem with a stochastic process driven by a bang-bang controller. We obtain the optimal times to switch the control signal that determines the drift rate of the process. For the cases handled in this thesis, the results suggest the optimality of the simplest event-triggered sampling policy with constant thresholds over the infinite horizon.