Artificial Intelligence and Machine Learning Driven Adaptive Control Applications

This article explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing adaptive control systems across various industries. Adaptive control systems adjust their parameters in real-time to maintain optimal performance in dynamic environments, and the integration of AI and ML significantly improves their effectiveness. AI enables these systems to learn, recognize patterns, and make autonomous decisions, while ML algorithms allow adaptive controllers to generalize from data, enhancing performance through techniques such as supervised, reinforcement, and unsupervised learning. Applications in autonomous vehicles, industrial process control, and aerospace illustrate the impact of AI and ML on adaptive control. Despite challenges such as computational demands, safety concerns, and regulatory hurdles, the future of adaptive control will be shaped by advances in AI and ML technologies, driving innovations in automation, precision, and reliability across diverse sectors.

Authors

Publication Details

Journal
Journal of Material Sciences and Engineering Technology
Published
2024-10-03
DOI
https://doi.org/10.61440/jmset.2024.v2.27
Citations
5
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
1.74
Controls
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article

Artificial Intelligence and Machine Learning Driven Adaptive Control Applications

Bahman Zohuri
5 citations
Journal of Material Sciences and Engineering Technology
Neural Networks and Applications
1.74
article

Artificial Intelligence and Machine Learning Driven Adaptive Control Applications

Bahman Zohuri
article en
5 citations

Abstract

This article explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing adaptive control systems across various industries. Adaptive control systems adjust their parameters in real-time to maintain optimal performance in dynamic environments, and the integration of AI and ML significantly improves their effectiveness. AI enables these systems to learn, recognize patterns, and make autonomous decisions, while ML algorithms allow adaptive controllers to generalize from data, enhancing performance through techniques such as supervised, reinforcement, and unsupervised learning. Applications in autonomous vehicles, industrial process control, and aerospace illustrate the impact of AI and ML on adaptive control. Despite challenges such as computational demands, safety concerns, and regulatory hurdles, the future of adaptive control will be shaped by advances in AI and ML technologies, driving innovations in automation, precision, and reliability across diverse sectors.

Journal of Material Sciences and Engineering Technology
Industry, innovation and infrastructure
Openalex Percentile: Top 13%
Neural Networks and Applications
1.74
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Artificial Intelligence and Machine Learning Driven Adaptive Control Applications — Bahman Zohuri · Journal of Material Sciences and Engineering Technology (2024) | TGRS Research Map | TGRS