Learning Robust State Observers using Neural ODEs (longer version)
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.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Cooperative Observation of Targets moving over a Planar Graph with Prediction of Positions
Consider a team with two types of agents: targets and observers. Observers are aerial UAVs that observe targets moving on land with their movements restricted to the paths that form a planar graph on the surface. Observe…
Resilient Interval Observer for Simultaneous Estimation of States, Modes and Attack Policies
This paper considers the problem of designing interval observers for hidden mode switched nonlinear systems with bounded noise signals that are compromised by false data injection and switching attacks. The proposed obse…
Robust Attitude Controller for Unmanned Aerial Vehicle Using Dynamic Inversion and Extended State Observer
A robust feedback linearization controller is presented for attitude control of an unmanned aerial vehicle (UAV). The objective of this controller is to make the roll angle, pitch angle, and yaw angle track the given tra…
Interturn Fault Detection in IPMSMs: Two Adaptive Observer-based Solutions
In this paper we address the problem of online detection of inter-turn short-circuit faults (ITSCFs) that occur in permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: (i) a very simple …
Fault Detectionparameter estimationFoveated Model Observers for Visual Search in 3D Medical Images
Model observers have a long history of success in predicting human observer performance in clinically-relevant detection tasks. New 3D image modalities provide more signal information but vastly increase the search space…