Deep Model Predictive Control with Online Learning for Complex Physical Systems

TL;DR

This paper introduces DeepMPC, a deep learning-based model predictive control framework that leverages low-dimensional flow features and online learning to effectively control complex, high-dimensional fluid systems in real-time.

Contribution

It presents a novel integration of RNN-based predictions with MPC that updates online, enabling efficient control of complex fluid flows without full state estimation.

Findings

DeepMPC outperforms traditional control methods in complex flow scenarios.

Online learning improves prediction accuracy and control performance.

The framework is validated on multiple fluid flow examples of increasing complexity.

Abstract

The control of complex systems is of critical importance in many branches of science, engineering, and industry. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high-dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems. We present a novel deep learning model predictive control (DeepMPC) framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system. The RNN is then embedded into a MPC framework to construct a feedback loop, and incoming sensor data is used to perform online updates to improve prediction accuracy. The results are validated using varying fluid flow examples of increasing complexity.