A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements
This addresses a specific challenge in control engineering for noisy nonlinear systems, representing an incremental improvement by extending physics-informed machine learning into control frameworks.
The paper tackles the problem of controlling nonlinear dynamic systems with highly noisy measurements, where existing data-driven methods fail. The proposed physics-informed machine learning method integrated with model predictive control outperforms state-of-the-art benchmarks in modeling accuracy and control performance for systems like the chaotic Lorenz 3 and turning machine tool under high-noise conditions.
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.