no code implementations • 15 Apr 2024 • Julian D. Schiller, Matthias A. Müller
We consider the case where observability of the parameters depends on the excitation of the system and may be absent during operation, with the parameter dynamics fulfilling a weak incremental bounded-energy bounded-state property to ensure boundedness of the estimation error (with respect to the disturbance energy).
no code implementations • 20 Dec 2023 • Julian D. Schiller, Matthias A. Müller
We propose a moving horizon estimation scheme to estimate the states and the unknown constant parameters of general nonlinear uncertain discrete time systems.
no code implementations • 11 May 2023 • Julian D. Schiller, Matthias A. Müller
We consider a moving horizon estimation (MHE) scheme involving a discounted least squares objective for general nonlinear continuous-time systems.
no code implementations • 9 May 2023 • Julian D. Schiller, Matthias A. Müller
We propose a time-discounted integral variant of incremental input/output-to-state stability (i-iIOSS) together with an equivalent Lyapunov function characterization.
no code implementations • 16 Nov 2022 • Julian D. Schiller, Matthias A. Müller
We propose a moving horizon estimation scheme for joint state and parameter estimation for nonlinear uncertain discrete-time systems.
no code implementations • 30 Mar 2022 • Julian D. Schiller, Boyang Wu, Matthias A. Müller
We propose a suboptimal moving horizon estimation (MHE) scheme for a general class of nonlinear systems.
no code implementations • 25 Feb 2022 • Julian D. Schiller, Simon Muntwiler, Johannes Köhler, Melanie N. Zeilinger, Matthias A. Müller
We provide a novel robust stability analysis for moving horizon estimation (MHE) using a Lyapunov function.
no code implementations • 31 Aug 2021 • Julian D. Schiller, Matthias A. Müller
In this paper, we propose a suboptimal moving horizon estimator for a general class of nonlinear systems.
no code implementations • 17 Nov 2020 • Julian D. Schiller, Sven Knüfer, Matthias A. Müller
In this paper, we propose a suboptimal moving horizon estimator for nonlinear systems.