A Separation-Based Design to Data-Driven Control for Large-Scale Partially Observed Systems
Year: 2017
Type: preprint
Abstract: This paper studies the partially observed stochastic optimal control problem for systems with state dynamics governed by Partial Differential Equations (PDEs) that leads to an extremely large problem. First, an open-loop deterministic trajectory optimization problem is solved using a black box simulation model of the dynamical system. Next, a Linear Quadratic Gaussian (LQG) controller is designed for the nominal trajectory-dependent linearized system, which is identified using input-output experimental data consisting of the impulse responses of the optimized nominal system. A computational nonlinear heat example is used to illustrate the performance of the approach.
Source: arXiv (Cornell University)
Institution: Texas A&M University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G1072749480, CPS: Synergy: Collaborative Research: Holistic Control and Management of Industrial Wireless Processes, G1513559549, G1523888516, G1877023635, G2568697414, G260750634, NRI: A Model based Approach to Distributed Adaptive Sampling of Spatio-Temporally Varying Fields, G320573562, G4239955923, G5385673730, Emerging Frontiers of Science of Information, G6929621500, G7142813775, G7155574171, G7401185013, G7452299184, G8257659198
OpenAlex ID: W2736302675