AI Enhanced Control Engineering Methods
It addresses the integration of AI into control engineering, offering incremental improvements for practitioners in this domain.
The paper explores applying AI tools, particularly automatic differentiation, to control engineering for tasks like linearization, state estimation, and model predictive control, providing examples and results for each use case.
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.