8 papers
Small-gain analysis of exponential incremental input/output-to-state stability for large-scale distributed systems
Christian Gatke, Julian D. Schiller, Matthias A. Müller
We provide a detectability analysis for nonlinear large-scale distributed systems in the sense of exponential incremental input/output-to-state stability (i-IOSS). In particular, w…
Tuning the burn-in phase in training recurrent neural networks improves their performance
Julian D. Schiller, Malte Heinrich, Victor G. Lopez +1
Training recurrent neural networks (RNNs) with standard backpropagation through time (BPTT) can be challenging, especially in the presence of long input sequences. A practical alte…
Nonlinear moving horizon estimation for robust state and parameter estimation -- extended version
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. The proposed framewor…
Optimal state estimation: Turnpike analysis and performance results
Julian D. Schiller, Lars Grüne, Matthias A. Müller
In this paper, we introduce turnpike arguments in the context of optimal state estimation. In particular, we show that the optimal solution of the state estimation problem involvin…
Moving horizon estimation for nonlinear systems with time-varying parameters
Julian D. Schiller, Matthias A. Müller
We propose a moving horizon estimation scheme for estimating the states and time-varying parameters of nonlinear systems. We consider the case where observability of the parameters…
Event-triggered moving horizon estimation for nonlinear systems
Isabelle Krauss, Julian D. Schiller, Victor G. Lopez +1
This work proposes an event-triggered moving horizon estimation (ET-MHE) scheme for general nonlinear systems. The key components of the proposed scheme are a novel event-triggerin…