APECS: Adaptive Personalized Control System Architecture

2025-03-10

AI Summary

To address insufficient synergy between human operators and controllers, and the resulting challenges in closed-loop stability assurance, in human-in-the-loop control systems, this paper proposes an adaptive personalized control framework. Methodologically, it introduces a novel learnable constraint mechanism that integrates dynamic Lipschitz constraints with sector-boundedness theory, embedded directly into the training of feedforward neural networks; additionally, a worst-case weighted loss function is designed to jointly optimize safety and robustness. The key contribution lies in the first explicit incorporation of dynamic constraints into a personalized controller learning architecture—thereby simultaneously enhancing human–machine collaborative performance and guaranteeing closed-loop stability. Simulation results demonstrate that the proposed system achieves a 4.5% performance improvement over unassisted human operators and a 9% gain over an unconstrained baseline neural controller.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Intelligent Robots: Behavior Learning & Control
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper presents the Adaptive Personalized Control System (APECS) architecture, a novel framework for human-in-the-loop control. An architecture is developed which defines appropriate constraints for the system objectives. A method for enacting Lipschitz and sector bounds on the resulting controller is derived to ensure desirable control properties. An analysis of worst-case loss functions and the optimal loss function weighting is made to implement an effective training scheme. Finally, simulations are carried out to demonstrate the effectiveness of the proposed architecture. This architecture resulted in a 4.5% performance increase compared to the human operator and 9% to an unconstrained feedforward neural network trained in the same way.

Problem

• Develops a human-in-the-loop control system architecture
• Ensures control properties via Lipschitz and sector bounds
• Improves performance through optimal loss function weighting

Innovation

• Adaptive Personalized Control System Architecture
• Lipschitz and sector bounds method
• Optimal loss function weighting analysis