AI for Closed-Loop Control Systems -- New Opportunities for Modeling, Designing, and Tuning Control Systems
This work addresses the integration of AI into control systems for applications like production machines and vehicles, but it is incremental as it focuses on conceptual discussions rather than novel implementations.
The paper explores the potential of using artificial intelligence, specifically artificial neural networks, to model, design, and tune closed-loop control systems, highlighting opportunities and research directions without presenting specific experimental results or numerical outcomes.
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.