Control-relevant dynamic data reconciliation and parameter estimation

Enhancing model predictive control using dynamic data reconciliation

“…niques. Ramamurthi et al 1993 presented a successively lin-Ž . earized horizon-based estimator SLHE for dynamic data reconciliation in closed-loop systems.…”

Constrained process monitoring: Moving‐horizon approach

“…Bequette Ž . and coworkers Bequette, 1991;Ramamurthi et al, 1993 investigated moving-horizon strategies for state estimation as a Ž . logical extension of model-predictive control.…”

Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations

“…By definition By repeated application of the inverse triangle inequality and utilizing the observability condition, we obtain the inequality By the Lipschitz continuity of , we obtain the inequality (13) where the existence of the K-function follows from Fact 2.2. By the Lipschitz continuity of , (11a), and (12a), we obtain the inequality Likewise, By the Lipschitz continuity of , (11b), and (12b), we obtain the inequality Substituting into (13), we obtain the inequality Substituting the aforementioned expressions in (10), we obtain the inequality Collectively defining the terms on the right hand side of the inequality as a function , we obtain the following bound of the estimation error: Facts 2.2 and 2.3 guarantee is K-function as it is a positive linear combination and composition of the K-functions and .…”

“…Many researchers in the process systems area extended the work of Jang et al. Bequette et al [12], [13] investigated moving horizon strategies for state estimation as a logical extension of model predictive control. Edgar and coworkers [14], [15] investigated moving horizon strategies for nonlinear data reconciliation.…”