43 citations · 100 across the 16 of their papers we have counts for
16 papers
Turnpike properties in nonlinear system identification
Julian D. Schiller, Matthias A. Müller
We analyze the problem of learning general discrete-time nonlinear state-space models using the simulation error minimization (SEM) method. In this setting, model parameters are ty…
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…
Sufficient Conditions for Detectability of Approximately Discretized Nonlinear Systems
Seth Siriya, Julian D. Schiller, Victor G. Lopez +1
In many sampled-data applications, observers are designed based on approximately discretized models of continuous-time systems, where usually only the discretized system is analyze…
Performance guarantees for optimization-based state estimation using turnpike properties
Julian D. Schiller, Lars Grüne, and Matthias A. Müller
In this paper, we develop novel accuracy and performance guarantees for optimal state estimation of general nonlinear systems (in particular, moving horizon estimation, MHE). Our r…
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…
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…
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…
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…
On an integral variant of incremental input/output-to-state stability and its use as a notion of nonlinear detectability
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. Continuity of…
Robust stability of moving horizon estimation for continuous-time systems
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. Provided that the system is detec…
A moving horizon state and parameter estimation scheme with guaranteed robust convergence
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. We establish robust exponential convergence of…
A simple suboptimal moving horizon estimation scheme with guaranteed robust stability
Julian D. Schiller, Boyang Wu, Matthias A. Müller
We propose a suboptimal moving horizon estimation (MHE) scheme for a general class of nonlinear systems. To this end, we consider an MHE formulation that optimizes over the traject…
A Lyapunov function for robust stability of moving horizon estimation
Julian D. Schiller, Simon Muntwiler, Johannes Köhler +2
We provide a novel robust stability analysis for moving horizon estimation (MHE) using a Lyapunov function. Additionally, we introduce linear matrix inequalities (LMIs) to verify t…
Suboptimal nonlinear moving horizon estimation
Julian D. Schiller, Matthias A. Müller
In this paper, we propose a suboptimal moving horizon estimator for a general class of nonlinear systems. For the stability analysis, we transfer the "feasibility-implies-stability…
Robust Stability of Suboptimal Moving Horizon Estimation using an Observer-Based Candidate Solution
Julian D. Schiller, Sven Knüfer, Matthias A. Müller
In this paper, we propose a suboptimal moving horizon estimator for nonlinear systems. For the stability analysis we transfer the "feasibility-implies-stability/robustness" paradig…