APECS: Adaptive Personalized Control System Architecture
This work addresses control system design for personalized human-in-the-loop applications, but appears incremental as it builds on existing control and neural network methods.
The paper tackled the problem of human-in-the-loop control by proposing the APECS architecture, which resulted in a 4.5% performance increase over a human operator and 9% over an unconstrained neural network in simulations.
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.