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Seminar on Control Theory and Nonlinear Filtering
Learning Robust State Observers using Neural ODEs
Learning Robust State Observers using Neural ODEs
Organizer
Speaker
Time
Monday, September 4, 2023 2:30 PM - 3:00 PM
Venue
数学系理科楼A-203
Abstract
Relying on recent research results on Neural ODEs, this paper presents a methodology for the design of state observers for nonlinear systems based on Neural ODEs, learning Luenberger-like observers and their nonlinear extension (Kazantzis-Kravaris-Luenberger (KKL) observers) for systems with partially-known nonlinear dynamics and fully unknown nonlinear dynamics, respectively. In particular, for tuneable KKL observers, the relationship between the design of the observer and its trade-off between convergence speed and robustness is analysed and used as a basis for improving the robustness of the learning-based observer in training. We illustrate the advantages of this approach in numerical simulations.