AI for Closed-Loop Control Systems -- New Opportunities for Modeling, Designing, and Tuning Control Systems

TL;DR

This paper explores how artificial intelligence, especially neural networks, can be integrated into closed-loop control systems to improve modeling, design, and tuning, opening new research opportunities in control engineering.

Contribution

It identifies potential AI components within control system architectures and discusses their feasibility, advantages, and challenges for real-time, safety-critical applications.

Findings

AI can replace certain control system blocks with neural networks.

AI-based controllers can meet real-time and safety requirements.

Pros and cons of AI integration in control systems are analyzed.

Abstract

Control Systems, particularly closed-loop control systems (CLCS), are frequently used in production machines, vehicles, and robots nowadays. CLCS are needed to actively align actual values of a process to a given reference or set values in real-time with a very high precession. Yet, artificial intelligence (AI) is not used to model, design, optimize, and tune CLCS. This paper will highlight potential AI-empowered and -based control system designs and designing procedures, gathering new opportunities and research direction in the field of control system engineering. Therefore, this paper illustrates which building blocks within the standard block diagram of CLCS can be replaced by AI, i.e., artificial neuronal networks (ANN). Having processes with real-time contains and functional safety in mind, it is discussed if AI-based controller blocks can cope with these demands. By concluding the paper, the pros and cons of AI-empowered as well as -based CLCS designs are discussed, and possible research directions for introducing AI in the domain of control system engineering are given.