Johannes Köhler

Last Name

Köhler

First Name

Johannes

ORCID

0000-0002-5556-604X

Organisational unit

09563 - Zeilinger, Melanie / Zeilinger, Melanie

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Publications1 - 10 of 47

Nonlinear Functional Estimation: Functional Detectability and Full Information Estimation
Muntwiler, Simon; Köhler, Johannes; Zeilinger, Melanie N. (2025)
Automatica
We consider the design of functional estimators, i.e., approaches to compute an estimate of a nonlinear function of the state of a general nonlinear dynamical system subject to process noise based on noisy output measurements. To this end, we introduce a novel functional detectability notion in the form of incremental input/output-to-output stability (δ-IOOS). We show that δ-IOOS is a necessary condition for the existence of a functional estimator satisfying an input-to-output type stability property. Additionally, we prove that a system is functional detectable if and only if it admits a corresponding δ-IOOS Lyapunov function. Furthermore, δ-IOOS is shown to be a sufficient condition for the design of a stable functional estimator by introducing the design of a full information estimation (FIE) approach for functional estimation. Together, we present a unified framework to study functional estimation with a detectability condition, which is necessary and sufficient for the existence of a stable functional estimator, and a corresponding functional estimator design. The practical need for and applicability of the proposed functional estimator design is illustrated with a numerical example of a power system.
MHE under parametric uncertainty — Robust state estimation without informative data
Muntwiler, Simon; Köhler, Johannes; Zeilinger, Melanie N. (2025)
IEEE Transactions on Automatic Control
In this paper, we study joint state and parameter estimation for general nonlinear systems with uncertain parameters and persistent process and measurement noise. In particular, we are interested in stability properties of the resulting state estimate in the absence of persistency of excitation (PE). With a simple academic example, we show that existing moving horizon estimation (MHE) approaches for joint state and parameter estimation as well as classical adaptive observers can result in diverging state estimates in the absence of PE, even if the noise is small. We propose an MHE formulation involving a regularization based on a constant prior estimate of the unknown system parameters. Only assuming the existence of a stable state estimator, we prove that the proposed MHE approach results in practically robustly stable state estimates irrespective of PE. We discuss the relation of the proposed MHE formulation to state-of-the-art results from MHE and adaptive estimation. The properties of the proposed MHE approach are illustrated with a numerical example of a car with unknown tire friction parameters.
Predictive Control for Nonlinear Stochastic Systems: Closed-Loop Guarantees With Unbounded Noise
Köhler, Johannes; Zeilinger, Melanie N. (2025)
IEEE Transactions on Automatic Control
We present a stochastic model predictive control framework for nonlinear systems subject to unbounded process noise with closed-loop guarantees. First, we provide a conceptual shrinking-horizon framework that utilizes general probabilistic reachable sets and minimizes the expected cost. Then, we provide a tractable receding-horizon formulation that uses a nominal state to minimize a deterministic quadratic cost and satisfy tightened constraints. Our theoretical analysis demonstrates recursive feasibility, satisfaction of chance constraints, and bounds on the expected cost for the resulting closed-loop system. We provide a constructive design for probabilistic reachable sets of nonlinear continuously differentiable systems using stochastic contraction metrics and an assumed bound on the covariance matrices. Numerical simulations highlight the computational efficiency and theoretical guarantees of the proposed method. Overall, this paper provides a framework for computationally tractable stochastic predictive control with closed-loop guarantees for nonlinear systems with unbounded noise.
On stochastic MPC formulations with closed-loop guarantees: Analysis and a unifying framework
Köhler, Johannes; Geuss, Ferdinand; Zeilinger, Melanie N. (2023)
2023 62nd IEEE Conference on Decision and Control (CDC)
We investigate model predictive control (MPC) formulations for linear systems subject to i.i.d. stochastic disturbances with bounded support and chance constraints. Existing stochastic MPC formulations with closed-loop guarantees can be broadly classified in two separate frameworks: i) using robust techniques; ii) feasibility preserving algorithms. We investigate two particular MPC formulations representative for these two frameworks called robust-stochastic MPC and indirect feedback stochastic MPC. We provide a qualitative analysis, highlighting intrinsic limitations of both approaches in different edge cases. Then, we derive a unifying stochastic MPC framework that naturally includes these two formulations as limit cases. This qualitative analysis is complemented with numerical results, showcasing the advantages and limitations of each method.
Model Predictive Control for Linear Uncertain Systems Using Integral Quadratic Constraints
Schwenkel, Lukas; Köhler, Johannes; Müller, Matthias A.; et al. (2023)
IEEE Transactions on Automatic Control
In this work, we propose a tube-based model predictive control (MPC) scheme for state- and input-constrained linear systems subject to dynamic uncertainties characterized by dynamic integral quadratic constraints (IQCs). In particular, we extend the framework of $\rho$-hard IQCs for exponential stability analysis to external inputs. This result yields that the error between the true uncertain system and the nominal prediction model is bounded by an exponentially stable scalar system. In the proposed tube-based MPC scheme, the state of this error bounding system is predicted along with the nominal model and used as a scaling parameter for the tube size. We prove that this method achieves robust constraint satisfaction and input-to-state stability despite dynamic uncertainties and additive bounded disturbances. A numerical example demonstrates the reduced conservatism of this IQC approach compared to state-of-the-art robust MPC approaches for dynamic uncertainties.
Robust Nonlinear Optimal Control via System Level Synthesis
Leeman, Antoine; Köhler, Johannes; Zanelli, Andrea; et al. (2023)
arXiv
This paper addresses the problem of finite horizon constrained robust optimal control for nonlinear systems subject to norm-bounded disturbances. To this end, the underlying uncertain nonlinear system is decomposed based on a first-order Taylor series expansion into a nominal system and an error (deviation) described as an uncertain linear time-varying system. This decomposition allows us to leverage system level synthesis to optimize an affine error feedback while planning the nominal trajectory and ensuring robust constraint satisfaction for the nonlinear system. The proposed approach thereby results in a less conservative planning compared with state-of-the-art techniques. A tailored sequential quadratic programming strategy is proposed to solve the resulting nonlinear program efficiently. We demonstrate the benefits of the proposed approach to control the rotational motion of a rigid body subject to state and input constraints.
Embedded Hierarchical MPC for Autonomous Navigation
Benders, Dennis; Köhler, Johannes; Niesten, Thijs; et al. (2025)
IEEE Transactions on Robotics
To efficiently deploy robotic systems in society, mobile robots must move autonomously and safely through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby obstacles. However, the limited computation power available on typical embedded robotic systems, such as quadrotors, poses a challenge to running MPC in real time, including its most expensive tasks: constraints generation and optimization. To address this problem, we propose a novel hierarchical MPC scheme that consists of a planning and a tracking layer. The planner constructs a trajectory with a long prediction horizon at a slow rate, while the tracker ensures trajectory tracking at a relatively fast rate. We prove that the proposed framework avoids collisions and is recursively feasible. Furthermore, we demonstrate its effectiveness in simulations and lab experiments with a quadrotor that needs to reach a goal position in a complex static environment. The code is efficiently implemented on the quadrotor's embedded computer to ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC formulation, this allows us to increase the planning horizon by a factor of 5, which results in significantly better performance.
Multi-Objective Robust Controller Synthesis With Integral Quadratic Constraints in Discrete-Time
Schwenkel, Lukas; Köhler, Johannes; Müller, Matthias A.; et al. (2026)
International Journal of Robust and Nonlinear Control
This article presents a novel framework for the robust controller synthesis problem in discrete-time systems using dynamic Integral Quadratic Constraints (IQCs). We present an algorithm to minimize closed-loop performance measures such as the -norm, the energy-to-peak gain, the peak-to-peak gain, or a multiobjective mix thereof. While IQCs provide a powerful tool for modeling structured uncertainties and nonlinearities, existing synthesis methods are limited to the -norm, continuous-time systems, or special system structures. By minimizing the energy-to-peak and peak-to-peak gain, the proposed synthesis can be utilized to bound the peak of the output, which is crucial in many applications requiring robust constraint satisfaction, input-to-state stability, reachability analysis, or other pointwise-in-time bounds. Numerical examples demonstrate the robustness and performance of the controllers synthesized with the proposed algorithm.
A Model-Free Approach to Control Barrier Functions Using Funnel Control
Lanza, Lukas; Köhler, Johannes; Dennstaedt, Dario; et al. (2025)
IEEE Control Systems Letters
Control barrier functions (CBFs) are a popular approach to design feedback laws that achieve safety guarantees for nonlinear systems. The CBF-based controller design relies on the availability of a model to select feasible inputs from the set of CBF-based controls. In this letter, we develop a model-free approach to design CBF-based control laws, eliminating the need for knowledge of system dynamics or parameters. Specifically, we address safety requirements characterized by a time-varying distance to a reference trajectory in the output space and construct a CBF that depends only on the measured output. Utilizing this particular CBF, we determine a subset of CBF-based controls without relying on a model of the dynamics by using techniques from funnel control. The latter is a model-free high-gain adaptive control methodology, which achieves tracking guarantees via reactive feedback. In this letter, we discover and establish a connection between the modular controller synthesis via zeroing CBFs and model-free reactive feedback. The theoretical results are illustrated by a numerical simulation.
Safe Active Learning of Cerebrospinal Fluid Dynamics
Flürenbrock, Fabian; Köhler, Johannes; Schmid Daners, Marianne; et al. (2025)
NeurIPS 2025 Workshop on Learning from Time Series for Health
This paper presents a safe active learning framework for a clinically relevant class of nonlinear systems with time-varying and uncertain parameters. The framework aims to provide a systematic trade-off between three competing clinical objectives: regulation of physiological variables to a safe zone, learning of patient-specific parameters, and minimization of the medical intervention. To address these challenges, we integrate the covariance propagation of a Kalman filter used for patient parameter estimation into an optimization-based control algorithm and enforce a desired estimation accuracy by introducing a soft constraint on the predicted covariances. We demonstrate the potential of the safe active learning framework for healthcare applications in a case study on cerebrospinal fluid dynamics. Our proposed method improves patient monitoring and shunt therapy for the neurological condition hydrocephalus by doubling the parameter estimation accuracy while requiring less than half the rate of intervention compared to standard approaches.

Publications1 - 10 of 47

Johannes Köhler

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