Goal: We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledge-based layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledge-based layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.

中文翻译：

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使用监督控制架构增强闭环认知压力调节

目标：我们提出了新的监督控制架构来调节认知压力状态并关闭循环。方法：我们获取皮肤电导信号的潜在神经脉冲中存在的信息，并采用基于模型的控制技术在状态空间框架中闭合环路。为了提高性能，我们建立了一个有监督的基于知识的层来实时更新控制系统。在监督架构中，控制器参数正在实时更新。结果：统计分析证明了监督控制架构在改善闭环结果方面的效率，同时通过更优化的控制努力将压力水平保持在所需范围内。基于模型的方法将保证控制系统视角的标准，如稳定性和最优性，并且提出的监督知识层将进一步提高它们的效率。结论：这项计算机研究的结果验证了拟在现实世界中实施的拟议监督架构的熟练程度。