Abstract
We shall present approaches to optimize the design of a measurement system and to schedule dynamically a versatile measurement resource. The analysis is based on expressing the system management task as a dynamic programming problem in which the system state is partially observable. We shall review the well-known linear-quadratic-Gaussian case, and discuss and give example solutions of discrete state systems with approximate dynamic programming methods to solve such problems in practice. Design problem is then studied by assessing the long-term performance of control and how it depends on properties of measurements. A simple design example is presented for a discrete state system.
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