arXiv subjects

Dario Dennstädt

Publications and source records attributed to Dario Dennstädt.

13 recordsLinked to original sources

arXiv2026

A model-free approach to control barrier functions for higher-order systems

Control barrier functions (CBFs) are a widely applied modular tool to ensure safe operation of nonlinear dynamical control systems. However, for their construction accurate knowledge of the system dynamics is typically needed. This requirement was recently alleviated for relative-degree-one systems using techniques from prescribed performance control (PPC) or funnel control (FC). This article extends the model-free CBF design to nonlinear systems of arbitrary relative degree. Moreover, we show with a simple example that a straightforward extension of existing results for relative-degree-one systems fails. Instead, we utilize novel techniques from funnel control to characterize a subset of the controls satisfying a CBF condition without requiring a dynamic model or state measurement. Finally, we demonstrate the applicability of our results on a seven degrees of freedom robotic manipulator with relative degree two.

eess.SY↗

arXiv2026

Model Predictive Control for output tracking with prescribed performance

Model Predictive Control (MPC) offers a versatile framework for constraint handling and multi-objective optimisation, yet practical application faces challenges regarding initial and recursive feasibility, robustness against model mismatches, and sampled-data constraints. This thesis develops a novel MPC framework for a class of non-linear continuous-time systems governed by functional differential equations. It targets output tracking within prescribed error bounds while systematically overcoming these challenges. First, we introduce funnel MPC, an algorithm eliminating reliance on terminal conditions or restrictive long prediction horizons. Utilising funnel penalty functions -- state costs penalising tracking error deviations from time-varying boundaries -- this framework ensures feasibility while rigorously enforcing tracking performance guarantees. Next, we unify funnel MPC with model-free funnel feedback into a hybrid architecture. This synergises model-based optimisation with adaptive feedback compensation, achieving prescribed tracking performance despite structural model-plant mismatches, unmodelled dynamics, and unknown disturbances. To further enhance predictive accuracy, we integrate a data-driven learning framework that iteratively refines the model using system measurements, improving long-term performance without compromising robustness. Finally, we formalise the transition to sampled-data implementations. We derive explicit bounds on sampling rates and control effort to guarantee stability under piecewise constant control signals, a critical step for digital hardware deployment. By addressing feasibility, robustness, learning, and sampling, this thesis establishes a cohesive framework for output tracking within prescribed bounds, paving the way for future advances in learning-enhanced and robust MPC.

math.OC↗

arXiv2025

A model-free approach to control barrier functions using funnel control

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 paper, 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 paper, 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.

math.OC↗

arXiv2025

On Model Predictive Funnel Control with Equilibrium Endpoint Constraints

We propose model predictive funnel control, a novel model predictive control (MPC) scheme building upon recent results in funnel control. The latter is a high-gain feedback methodology that achieves evolution of the measured output within predefined error margins. The proposed method dynamically optimizes a parameter-dependent error boundary in a receding-horizon manner, thereby combining prescribed error guarantees from funnel control with the predictive advantages of MPC. On the one hand, this approach promises faster optimization times due to a reduced number of decision variables, whose number does not depend on the horizon length. On the other hand, the continuous feedback law improves the robustness and also explicitly takes care of the inter-sampling behavior. We focus on proving stability by leveraging results from MPC stability theory with terminal equality constraints. Moreover, we rigorously show initial and recursive feasibility.

math.OC↗

arXiv2025

A low-complexity funnel control approach for non-linear systems of higher-order

We address the problem of output reference tracking for unknown non-linear multi-input, multi-output systems described by functional differential equations. This class of systems includes those with a strict relative degree, and bounded-input bounded-output (BIBO) stable internal dynamics. The objective is to ensure that the tracking error evolves within a prescribed performance funnel. To achieve this, we propose a novel model-free adaptive controller with lower complexity than existing funnel control methods, avoiding the use of non-linearities in intermediate error signals. We establish the feasibility and effectiveness of the proposed design through theoretical analysis and demonstrate its performance with simulations, comparing it to previous approaches.

math.OC↗

arXiv2025

Safe continual learning in model predictive control with prescribed bounds on the tracking error

We develop a three-component Model Predictive Control (MPC) algorithm to achieve output-reference tracking with prescribed performance for continuous-time nonlinear systems. One component is so-called funnel MPC, which achieves reference tracking with prescribed performance for the model output for suitable models. Recently, this MPC algorithm has been combined with a model-free reactive feedback controller (second component) to account for model-plant mismatches, bounded disturbances, and uncertainties. By construction, this two-component controller defines a robust funnel MPC algorithm. It achieves output-reference tracking within prescribed bounds on the tracking error for a class of unknown nonlinear systems. In this paper, we extend the robust funnel MPC by a machine learning component to adapt the underlying model to the system data and, thus, improve the contribution of MPC. We derive sufficient structural conditions to define a class of models for funnel MPC, and provide a characterization of suitable learning schemes. Since robust funnel MPC is inherently robust and the evolution of the tracking error in the prescribed performance funnel is guaranteed, the additional learning component is able to perform the learning task online -- even without an initial model or offline training.

math.OC↗

arXiv2025

Sampled-data funnel control and its use for safe continual learning

We propose a novel sampled-data output-feedback controller for nonlinear systems of arbitrary relative degree that ensures reference tracking within prescribed error bounds. We provide explicit bounds on the maximum input signal and the required uniform sampling time. A key strength of this approach is its capability to serve as a safety filter for various learning-based controller designs, enabling the use of learning techniques in safety-critical applications. We illustrate its versatility by integrating it with two different controllers: a reinforcement learning controller and a non-parametric predictive controller based on Willems et al.'s fundamental lemma. Numerical simulations illustrate effectiveness of the combined controller design.

math.OC↗

arXiv2024

On model predictive control with sampled-data input for output tracking with prescribed performance

We propose a model predictive control (MPC) scheme with sampled-data input which ensures output-reference tracking within prescribed error bounds for relative-degree-one systems. Hereby, we explicitly deduce bounds on the required maximal control input and sampling frequency such that the MPC scheme is both initially and recursively feasible. A key feature of the proposed approach is that neither terminal conditions nor a sufficiently-large prediction horizon are imposed, rendering the MPC scheme computationally efficient. We illustrate the MPC algorithm via a numerical example of a torsional oscillator.

math.OC↗

arXiv2024

Funnel MPC for nonlinear systems with arbitrary relative degree

The Model Predictive Control (MPC) scheme Funnel MPC enables output tracking of smooth reference signals with prescribed error bounds for nonlinear multi-input multi-output systems with stable internal dynamics. Earlier works achieved the control objective for system with relative degree restricted to one or incorporated additional feasibility constraints in the optimal control problem. Here we resolve these limitations by introducing a modified stage cost function relying on a weighted sum of the tracking error derivatives. The weights need to be sufficiently large and we state explicit lower bounds. Under these assumptions we are able to prove initial and recursive feasibility of the novel Funnel MPC scheme for systems with arbitrary relative degree - without requiring any terminal conditions, a sufficiently long prediction horizon or additional output constraints.

math.OC↗

arXiv2023

Robust Funnel Model Predictive Control for output tracking with prescribed performance

We propose a novel robust Model Predictive Control (MPC) scheme for nonlinear multi-input multi-output systems of relative degree one with stable internal dynamics. The proposed algorithm is a combination of funnel MPC, i.e., MPC with a particular stage cost, and the model-free adaptive funnel controller. The new robust funnel MPC scheme guarantees output tracking of reference signals within prescribed performance bounds -- even in the presence of unknown disturbances and a structural model-plant mismatch. We show initial and recursive feasibility of the proposed control scheme without imposing terminal conditions or any requirements on the prediction horizon. Moreover, we allow for model updates at runtime. To this end, we propose a proper initialization strategy, which ensures that recursive feasibility is preserved. Finally, we validate the performance of the proposed robust MPC scheme by simulations.

math.OC↗

arXiv2022

Funnel MPC with feasibility constraints for nonlinear systems with arbitrary relative degree

We study tracking control for nonlinear systems with known relative degree and stable internal dynamics by the recently introduced technique of Funnel MPC. The objective is to achieve the evolution of the tracking error within a prescribed performance funnel. We propose a novel stage cost for Funnel MPC, extending earlier designs to the case of arbitrary relative degree, and show that the control objective as well as initial and recursive feasibility are always achieved - without requiring any terminal conditions or a sufficiently long prediction horizon. We only impose an additional feasibility constraint in the optimal control problem.

math.OC↗

arXiv2022

Funnel MPC for nonlinear systems with relative degree one

We show that Funnel MPC, a novel Model Predictive Control (MPC) scheme, allows tracking of smooth reference signals with prescribed performance for nonlinear multi-input multi-output systems of relative degree one with stable internal dynamics. The optimal control problem solved in each iteration of Funnel MPC resembles the basic idea of penalty methods used in optimization. To this end, we present a new stage cost design to mimic the high-gain idea of (adaptive) funnel control. We rigorously show initial and recursive feasibility of Funnel MPC without imposing terminal conditions or other requirements like a sufficiently long prediction horizon.

math.OC↗

arXiv2022

Towards Funnel MPC for nonlinear systems with relative degree two

Funnel MPC, a novel Model Predictive Control (MPC) scheme, allows guaranteed output tracking of smooth reference signals with prescribed error bounds for nonlinear multi-input multi-output systems. To this end, the stage cost resembles the high-gain idea of funnel control. However, rigorous proofs for initial and recursive feasibility without incorporating additional output constraints in the Funnel MPC scheme are only available for systems with relative degree one and stable internal dynamics. In this paper, we extend these results to systems with relative degree two by incorporating also a term based on the idea of a derivative funnel in the stage cost.

math.OC↗