"Optimizing Automation Through Feedback Loops: Enhancing Control Systems with Adaptive Techniques"

EasyChair Preprint 14358

Abstract

The integration of feedback loops in automation systems plays a critical role in enhancing control mechanisms and overall system efficiency. This paper explores advanced techniques for optimizing automation through adaptive feedback loops, focusing on how these methods improve performance, reliability, and responsiveness in control systems. By examining various adaptive algorithms and their application in real-world scenarios, the study highlights the potential for dynamic adjustments based on real-time data to refine control strategies. Key findings include the effectiveness of adaptive feedback in mitigating system instability, reducing error rates, and enabling more precise control. The research underscores the importance of continuous learning and adjustment in automation systems, proposing new frameworks for implementing adaptive feedback mechanisms to achieve superior operational outcomes. The paper concludes with recommendations for future research and practical applications, emphasizing the need for ongoing innovation in control system design to harness the full potential of adaptive techniques.

Keyphrases: Information Processing Systems, adaptive algorithms, machine learning, self-regulation

@booklet{EasyChair:14358,
  author    = {John Owen},
  title     = {"Optimizing Automation Through Feedback Loops: Enhancing Control Systems with Adaptive Techniques"},
  howpublished = {EasyChair Preprint 14358},
  year      = {EasyChair, 2024}}