Scheduling Dimension Reduction of LPV Models - A Deep Neural Network Approach

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

5 Downloads (Pure)

Samenvatting

In this paper, the existing Scheduling Dimension Reduction (SDR) methods for Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network (DNN) approach is developed that achieves higher model accuracy under scheduling dimension reduction. The proposed DNN method and existing SDR methods are compared on a two-link robotic manipulator, both in terms of model accuracy and performance of controllers synthesized with the reduced models. The methods compared include SDR for state-space models using Principal Component Analysis (PCA), Kernel PCA (KPCA) and Autoencoders (AE). On the robotic manipulator example, the DNN method achieves improved representation of the matrix variations of the original LPV model in terms of the Frobenius norm compared to the current methods. Moreover, when the resulting model is used to accommodate synthesis, improved closed-loop performance is obtained compared to the current methods.
Originele taal-2Engels
Titel2020 American Control Conference (ACC), Denver
StatusGeaccepteerd/In druk - 14 mei 2020

Bibliografische nota

Accepted to American Control Conference (ACC) 2020, Denver

Trefwoorden

  • eess.SY
  • cs.SY

Vingerafdruk Duik in de onderzoeksthema's van 'Scheduling Dimension Reduction of LPV Models - A Deep Neural Network Approach'. Samen vormen ze een unieke vingerafdruk.

  • Citeer dit

    Koelewijn, P. J. W., & Tóth, R. (Geaccepteerd/In druk). Scheduling Dimension Reduction of LPV Models - A Deep Neural Network Approach. In 2020 American Control Conference (ACC), Denver