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APECS: Adaptive Personalized Control System Architecture

arXiv 2503.09624 ↗First posted 10 Mar 2025eess.SY cs.LG cs.ROcited by 0

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Abstract (arXiv)

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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References · 16

  1. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  2. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  3. 2017Spectrally-normalized margin bounds for neural networkscited by 686
  4. 2018Regularisation of Neural Networks by Enforcing Lipschitz Continuitycited by 314
  5. 2019Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networkscited by 293
  6. 2018Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networkscited by 195
  7. 2021Globally-Robust Neural Networkscited by 108
  8. 2021Orthogonalizing Convolutional Layers with the Cayley Transformcited by 105
  9. 2020Lipschitz constant estimation of Neural Networks via sparse polynomial optimizationcited by 87
  10. 2021Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Boundscited by 65
  11. 2021A Dynamical System Perspective for Lipschitz Neural Networkscited by 60
  12. 2023A Unified Algebraic Perspective on Lipschitz Neural Networkscited by 60
4 more
  1. 2022Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networkscited by 39
  2. 2018PythonRobotics: a Python code collection of robotics algorithmscited by 12
  3. 2020Decision-Making in Driver-Automation Shared Control: A Review and Perspectivescited by 7
  4. 2019Personalized On-line Adaptation of Kinematic Synergies for Human-Prosthesis Interfacescited by 1

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