Some manifold learning considerations toward explicit model predictive control
Abstract
Model predictive control (MPC) is a de facto standard control algorithm across the process industries. There remain, however, applications where MPC is impractical because an optimization problem is solved at each time step. We present a link between explicit MPC formulations and manifold learning to enable facilitated prediction of the MPC policy. Our method uses a similarity measure informed by control policies and system state variables, to “learn” an intrinsic parametrization of the MPC controller using a diffusion maps algorithm, which will also discover a low‐dimensional control law when it exists as a smooth, nonlinear combination of the state variables. We use function approximation algorithms to project points from state space to the intrinsic space, and from the intrinsic space to policy space. The approach is illustrated first by “learning” the intrinsic variables for MPC control of constrained linear systems, and then by designing controllers for an unstable nonlinear reactor.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 10, 2020
- Source ID
- 10.1002/aic.16881
Entities
People
- Felix Dietrich
- Robert J Lovelett
- Seungjoon Lee
- Yannís G. Kevrekidis
Organizations
- Defense Advanced Research Projects Agency
- Johns Hopkins University
- Princeton University