A Separation-based Approach to Data-based Control for Large-Scale Partially Observed Systems

TL;DR

This paper introduces a separation-based control method for large-scale partially observed systems governed by PDEs, combining trajectory optimization with data-driven LQG control design, demonstrated on a nonlinear heat example.

Contribution

It presents a novel approach that integrates trajectory optimization and data-driven LQG control for large-scale PDE systems with partial observations.

Findings

Effective control achieved on a nonlinear heat system

Combines simulation-based optimization with experimental data

Demonstrates scalability to large PDE systems

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 proposed approach.