Physics Informed Neural Network for feedforward control of a 2-DOF manipulator with flexure joints

Bram Harbers, R.G.K.M. Aarts

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Abstract

Feedforward control of a manipulator can be generated with a sufficiently accurate stable inverse model of the manipulator. It has been demonstrated before that a Lagrangian Neural Network (LNN), or Deep Lagrangian Networks (DeLaN), can be trained to estimate the conservative part of the driving forces for a specified trajectory. Such network is bound to physical constraints and hence can predict the (inverse) multibody system behaviour quite accurately and robustly from a relatively small dataset. However, it does not account for non-conservative contributions to the forces.
To include damping and friction in the estimates, this paper proposes to include additional terms in the underlying equations to obtain a so-called DeLaN+D. The performance of this network is evaluated with simulated and experimental data from a fully actuated 2-DOF manipulator with flexure joints. The achievable accuracy of the predicted feedforward forces appears to be better than 97% in experiments with a validation trajectory. The tracking accuracy during controlled motion is improved with about 80% using feedforward control with this DeLaN+D, which is comparable to using identified inverse multibody system dynamics.
Original languageEnglish
Number of pages2
Publication statusPublished - 22 May 2024
Event7th International Conference on Multibody System Dynamics, IMSD 2024 - Memorial Union, 800 Langdon St, Madison, WI 53703, Madison, United States
Duration: 9 Jun 202413 Jun 2024
Conference number: 7
https://imsd2024.engineering.wisc.edu/

Conference

Conference7th International Conference on Multibody System Dynamics, IMSD 2024
Abbreviated titleIMSD 2024
Country/TerritoryUnited States
CityMadison
Period9/06/2413/06/24
Internet address

Keywords

  • feedforward control
  • flexible multibody system
  • Flexure joints
  • Physics-informed neural network

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