arXiv subjects

Joscha Bongard

Publications and source records attributed to Joscha Bongard.

3 recordsLinked to original sources

arXiv2026

Head-to-Head autonomous racing at the limits of handling in the A2RL challenge

Autonomous racing presents a complex challenge involving multi-agent interactions between vehicles operating at the limit of performance and dynamics. As such, it provides a valuable research and testing environment for advancing autonomous driving technology and improving road safety. This article presents the algorithms and deployment strategies developed by the TUM Autonomous Motorsport team for the inaugural Abu Dhabi Autonomous Racing League (A2RL). We showcase how our software emulates human driving behavior, pushing the limits of vehicle handling and multi-vehicle interactions to win the A2RL. Finally, we highlight the key enablers of our success and share our most significant learnings.

cs.RO↗

arXiv2024

A Discussion on Nonlinear Quadratic Control and Sontag's Formula

The quadratic optimal state feedback (LQR) is one of the most popular designs for linear systems and succeeds via the solution of the algebraic Riccati equation. The situation is different in the case of non-linear systems: the Riccati equation is then replaced by the Hamilton Jacobi Bellman equation (HJB), the solution of which is generally difficult. A compromise can be the so-called Inverse Optimal Control, a form of which is Sontag's formula [1]; here the minimized cost function follows from the feedback law chosen, not vice versa. Using Sontag's formula in the variant according to Freeman and Primbs [2, 9], the actually minimized cost function is given in the following sections, including cases when it reduces to the quadratic cost. Also some remarks and thoughts are presented for discussion.

math.OC↗

arXiv2022

Robust stability analysis of a simple data-driven model predictive control approach

In this paper, we provide a theoretical analysis of closed-loop properties of a simple data-driven model predictive control (MPC) scheme. The formulation does not involve any terminal ingredients, thus allowing for a simple implementation without (potential) feasibility issues. The proposed approach relies on an implicit description of linear time-invariant systems based on behavioral systems theory, which only requires one input-output trajectory of an unknown system. For the nominal case with noise-free data, we prove that the data-driven MPC scheme ensures exponential stability for the closed loop if the prediction horizon is sufficiently long. Moreover, we analyze the robust data-driven MPC scheme for noisy output measurements for which we prove closed-loop practical exponential stability. The advantages of the presented approach are illustrated with a numerical example.

math.OC↗