AI Enhanced Control Engineering Methods
Ion Matei Raj Minhas Johan de Kleer Alexander Felman
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.