Optimal output estimation for infinite-dimensional systems with disturbances

Morris, Kirsten

doi:10.1016/j.sysconle.2020.104803

Cited by 4 publications

(1 citation statement)

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“…For numerical approximation and computational issues, approximation results are summarized in [8] for the infinite-dimensional algebraic Riccati equations of a linear-quadratic regulator. However, often the distur-bance to a system may not be Gaussian, in which case an H 2 -or H ∞ -observer design is favorable [31,41].…”

Section: Introductionmentioning

confidence: 99%

Optimal guidance and estimation of a 1D diffusion process by a team of mobile sensors

Cheng

Paley

2020

2020 59th IEEE Conference on Decision and Control (CDC)

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This paper describes an optimization framework to design guidance for a possibly heterogeneous team of multiple mobile sensors to estimate a spatiotemporal process modeled by a 2D diffusion-advection process. Owing to the abstract linear system representation of the process, we apply the Kalman-Bucy filter for estimation, where the sensors provide linear outputs. We propose an optimization problem that minimizes the sum of the trace of the covariance operator of the Kalman-Bucy filter and a generic mobility cost of the mobile sensors, subject to the sensors' motion modeled by linear dynamics. We establish the existence of a solution to this problem. Moreover, we prove convergence to the exact optimal solution of the approximate optimal solution. That is, when evaluating these two solutions using the original cost function, the difference becomes arbitrarily small as the approximation gets finer. To compute the approximate solution, we use Pontryagin's minimum principle after approximating the infinite-dimensional terms originating from the diffusion-advection process. The approximate solution is applied in simulation to analyze how a single mobile sensor's performance depends on two important parameters: sensor noise variance and mobility penalty. We also illustrate the application of the framework to multiple sensors, in particular the performance of a heterogeneous team of sensors.

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Section: Introductionmentioning

confidence: 99%