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Communication Dans Un Congrès Année : 2023

Redundancy-aware physics informed neural networks (R-PINNs) based learning of nonlinear algebraic systems with non-measurable states

Résumé

The paper presents Redundancy-Aware Physics Informed Neural Networks (R-PINNs) for learning of unknown model parameters of nonlinear algebraic systems in continuous time with non-measurable state variables. R-PINNs accomplish the learning task in presence of non-measurable states of the system by incorporating input-output representation of the a priori available physics based laws, generally in form of nonlinear differential (partial) equations within the NN based learning procedure, leading to learning of a set of optimal parameters that determine the optimal mapping between input-output data while adhering to the known physics. Analytical Redundancy Relationships (ARRs) are able to express input-output representation of system using solely the measured/known variables by exploring the redundancy within the analytical structure of the system. The paper proposes a methodology that includes ARR derivation and suitable integration within PINNs framework to develop R-PINNs. Mathematically rigorous novel proofs on uniform and ultimate boundedness (UUB) of the output and parametric estimation errors in Lyapunov sense is provided. Finally a DC motor enabled friction drive system based simulation study is presented to demonstrate the effectiveness of the approach.
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Dates et versions

hal-04307009 , version 1 (25-11-2023)

Identifiants

  • HAL Id : hal-04307009 , version 1

Citer

Mayank Shekhar Jha, Hugues Garnier, Didier Theilliol. Redundancy-aware physics informed neural networks (R-PINNs) based learning of nonlinear algebraic systems with non-measurable states. 62nd IEEE Conference on Decision and Control, CDC 2023, Dec 2023, Marina Bay Sands, Singapore. ⟨hal-04307009⟩
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