The Entropy Penalized Minimum Energy Estimator

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Sergio Pequito (Author) Other; Pedro Aguiar, AP (Author) Other; Diogo A Gomes (Author) Other

Conference proceedings International

Title: PROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009)

Pages: 1285-1290

Joint 48th IEEE Conference on Decision and Control (CDC) / 28th Chinese Control Conference (CCC)

Shanghai, PEOPLES R CHINA, DEC 15-18, 2009

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-007-SY0

DOI: 10.1109/cdc.2009.5400482

Abstract (EN): This paper addresses the state estimation problem of nonlinear systems. We formulate the problem using a minimum energy estimator (MEE) approach and propose an entropy penalized scheme to approximate the viscosity solution of the Hamilton-Jacobi equation that follows from the MEE formulation. We derive an explicit observer algorithm that is iterative and filtering-like, which continuously improves the state estimation as more measurements arise. In addition, we propose a computationally efficient procedure to estimate the state by performing an approximation of the nonlinear system along the trajectory of the estimate. In this case, for the first and second order approximations of the state equation, we derive a closed-form (iterative) solution for the Hessian of the entropy-like version of the optimal cost function of the MEE. We illustrate and contrast the performance of our algorithms with the extended Kalman filter (EKF) using specific nonlinear examples with the feature that the EKF do not converge to the correct value.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6