A New Identification Framework For Off-Line Computation of Moving-Horizon Observers
For control systems requiring real-time state estimation, this framework reduces online computational burden, but the improvement is incremental.
The paper proposes a nonlinear identification framework that enables off-line computation of moving-horizon observers by combining nonlinear approximators with constrained quadratic programming, achieving bounded estimation error demonstrated on two examples.
In this paper, a new nonlinear identification framework is proposed to address the issue of off-line computation of moving-horizon observer estimate. The proposed structure merges the advantages of nonlinear approximators with the efficient computation of constrained quadratic programming problems. A bound on the estimation error is proposed and the efficiency of the resulting scheme is illustrated using two state estimation examples.