A Separation-Based Design to Data-Driven Control for Large-Scale Partially Observed Systems
This addresses control challenges for large-scale partially observed PDE systems, but appears incremental as it integrates existing methods.
The paper tackles the partially observed stochastic optimal control problem for large-scale systems governed by PDEs by combining open-loop trajectory optimization with data-driven LQG control, demonstrating performance through a computational heat example.
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.