  This study presents a physics-informed machine learning-based control method
for nonlinear dynamic systems with highly noisy measurements. Existing
data-driven control methods that use machine learning for system identification
cannot effectively cope with highly noisy measurements, resulting in unstable
control performance. To address this challenge, the present study extends
current physics-informed machine learning capabilities for modeling nonlinear
dynamics with control and integrates them into a model predictive control
framework. To demonstrate the capability of the proposed method we test and
validate with two noisy nonlinear dynamic systems: the chaotic Lorenz 3 system,
and turning machine tool. Analysis of the results illustrate that the proposed
method outperforms state-of-the-art benchmarks as measured by both modeling
accuracy and control performance for nonlinear dynamic systems under high-noise
conditions.
