Risk-Sensitive Extended Kalman Filter - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2023

Risk-Sensitive Extended Kalman Filter

Abstract

In robotics, designing robust algorithms in the face of estimation uncertainty is a challenging task. Indeed, controllers often do not consider the estimation uncertainty and only rely on the most likely estimated state. Consequently, sudden changes in the environment or the robot's dynamics can lead to catastrophic behaviors. In this work, we present a risk-sensitive Extended Kalman Filter that allows doing outputfeedback Model Predictive Control (MPC) safely. This filter adapts its estimation to the control objective. By taking a pessimistic estimate concerning the value function resulting from the MPC controller, the filter provides increased robustness to the controller in phases of uncertainty as compared to a standard Extended Kalman Filter (EKF). Moreover, the filter has the same complexity as an EKF, so that it can be used for real-time model-predictive control. The paper evaluates the risk-sensitive behavior of the proposed filter when used in a nonlinear model-predictive control loop on a planar drone and industrial manipulator in simulation, as well as on an external force estimation task on a real quadruped robot. These experiments demonstrate the abilities of the approach to improve performance in the face of uncertainties significantly.

Domains

Automatic
Fichier principal
Vignette du fichier
Risk-Sensitive Extended Kalman Filter.pdf (1016.33 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04107207 , version 1 (26-05-2023)

Identifiers

  • HAL Id : hal-04107207 , version 1

Cite

Armand Jordana, Avadesh Meduri, Etienne Arlaud, Justin Carpentier, Ludovic Righetti. Risk-Sensitive Extended Kalman Filter. 2023. ⟨hal-04107207⟩
25 View
75 Download

Share

Gmail Mastodon Facebook X LinkedIn More