Sparse Identification of Nonlinear Dynamics with Control (SINDYc)
arXiv:1605.06682
Abstract
Identifying governing equations from data is a critical step in the modeling and control of complex dynamical systems. Here, we investigate the data-driven identification of nonlinear dynamical systems with inputs and forcing using regression methods, including sparse regression. Specifically, we generalize the sparse identification of nonlinear dynamics (SINDY) algorithm to include external inputs and feedback control. This method is demonstrated on examples including the Lotka-Volterra predator--prey model and the Lorenz system with forcing and control. We also connect the present algorithm with the dynamic mode decomposition (DMD) and Koopman operator theory to provide a broader context.
6 pages, 4 figures, Accepted for NOLCOS conference
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- Vehicular Applications of Koopman Operator Theory -- A Survey
- Learning physically consistent mathematical models from data using group sparsity
- Model Order Reduction in Neuroscience
- Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
- Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
- Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems
- Constrained Sparse Galerkin Regression
- Restart of accelerated first order methods with linear convergence under a quadratic functional growth condition
- Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering