A fast moving horizon estimation algorithm based on nonlinear programming sensitivity


Abstract:

Moving horizon estimation (MHE) is an efficient optimization-based strategy for state estimation. Despite the attractiveness of this method, its application in industrial settings has been rather limited. This has been mainly due to the difficulty to solve, in real-time, the associated dynamic optimization problems. In this work, a fast MHE algorithm able to overcome this bottleneck is proposed. The strategy exploits recent advances in nonlinear programming algorithms and sensitivity concepts. A detailed analysis of the optimality conditions of MHE problems is presented. As a result, strategies for fast covariance information extraction from general nonlinear programming algorithms are derived. It is shown that highly accurate state estimates can be obtained in large-scale MHE applications with negligible on-line computational costs. © 2008 Elsevier Ltd. All rights reserved.

Año de publicación:

2008

Keywords:

  • real-time
  • Estimation algorithms
  • nonlinear programming
  • Large-scale
  • Sensitivity Analysis

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso restringido

Áreas de conocimiento:

  • Algoritmo
  • Algoritmo
  • Algoritmo

Áreas temáticas de Dewey:

  • Métodos informáticos especiales
Procesado con IAProcesado con IA

Objetivos de Desarrollo Sostenible:

  • ODS 9: Industria, innovación e infraestructura
  • ODS 12: Producción y consumo responsables
  • ODS 8: Trabajo decente y crecimiento económico
Procesado con IAProcesado con IA