AI Enhanced Control Engineering Methods

• URL: http://arxiv.org/abs/2306.05545v1
• Date: Thu, 8 Jun 2023 20:31:14 GMT
• Title: AI Enhanced Control Engineering Methods
• Authors: Ion Matei, Raj Minhas, Johan de Kleer and Alexander Felman
• Abstract summary: We explore how AI tools can be useful in control applications. Two immediate applications are linearization of system dynamics for local stability analysis or for state estimation using Kalman filters. In addition, we explore the use of machine learning models for global parameterizations of state vectors and control inputs in model predictive control applications.
• Score: 66.08455276899578
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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