Descent algorithms based on the gradient,conjugate gradient, and Newton methods are investigated to perform optimization in moving horizon state estimation for discrete-time linear and nonlinear systems. Conditions that ensure the stability of the estimation error are established for single and multi iteration schemes with a least-squares cost function that takes into account only a batch of most recent information. Simulation results show the effectiveness of the proposed approaches also in comparison with techniques based on the Kalman filter.
|Titolo:||Fast moving horizon state estimation for discrete-time systems using single and multi iteration descent methods|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||01.01 - Articolo su rivista|