AI Enhanced Control Engineering Methods

TL;DR

This paper discusses how AI tools, especially automatic differentiation and machine learning, can enhance control engineering by improving system analysis, modeling, and control strategies with practical examples.

Contribution

It introduces novel applications of AI in control engineering, including system linearization, differential equation conversion, and global parameterization in predictive control.

Findings

Automatic differentiation aids in system linearization.

Machine learning improves model predictive control.

Conversion of differential algebraic equations enhances control design.

Abstract

AI and machine learning based approaches are becoming ubiquitous in almost all engineering fields. Control engineering cannot escape this trend. In this paper, we explore how AI tools can be useful in control applications. The core tool we focus on is automatic differentiation. Two immediate applications are linearization of system dynamics for local stability analysis or for state estimation using Kalman filters. We also explore other usages such as conversion of differential algebraic equations to ordinary differential equations for control design. In addition, we explore the use of machine learning models for global parameterizations of state vectors and control inputs in model predictive control applications. For each considered use case, we give examples and results.