提出自适应个性化控制系统，提升人机协同控制性能。
APECS: Adaptive Personalized Control System Architecture
- 通过Lipschitz和扇区约束设计控制器，保证控制稳定性。
- 在相同训练条件下，性能优于人类操作员4.5%、无约束神经网络9%。
- 适合需要高可靠性人机协作的智能系统场景。
本文提出自适应个性化控制系统（APECS）架构，一种面向人机协同控制的新框架。该架构定义了系统目标的合理约束，并推导出对控制器施加Lipschitz和扇区边界的方法，以确保期望的控制特性。通过分析最坏情况损失函数及最优损失权重，构建了有效的训练方案。最终仿真验证表明，该架构在相同训练条件下，相比人类操作员性能提升4.5%，相较于无约束前馈神经网络提升9%。
原文摘要 · Abstract (English)
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.
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