PhD Candidate in Mechanical and Process Engineering
Institute for Dynamic Systems and Control
ETH Zürich

Amon Lahr was born in Berlin, Germany, in 1996. He completed his Bachelor’s studies in Engineering
Science in 2018, with two semester-long stays at New York University and the German Aerospace
Center in Stuttgart, Germany, respectively. Redirecting his study focus towards numerical mathematics, control theory and model reduction, Amon later received a Master’s degree in Scientific Computing from TU Berlin in 2021, with his thesis on “ℋ-∞ Control for Large-Scale Linear Systems”. During his studies, Amon has worked as a Linux and web developer for IoT devices in the automotive industry, shaping his interests in embedded systems and data-driven control methods.
Project description
While the performance and potential of learning-based control has been recently demonstrated, the associated computational challenges remain a key limiting factor for moving these techniques into industrial applications. On embedded hardware in particular, the feasible model complexity and sampling times are restricted by the limited storage and computational power. The aim of this PhD project is to develop new controllers and computational methods for embedded control systems. Possible research directions include the development of tailored real-time optimization routines, controller approximation using learning-based function approximation schemes, as well as efficient data selection and reduction.
Publications
Lahr, Amon; Näf, Joshua; Wabersich, Kim P.; Frey, Jonathan; Siehl, Pascal; Carron, Andrea; Diehl, Moritz; Zeilinger, Melanie N.
L4acados: Learning-based Models for Acados, Applied to Gaussian Process-Based Predictive Control Working paper
2024.
@workingpaper{lahr_l4acados_2024,
title = {L4acados: Learning-based Models for Acados, Applied to Gaussian Process-Based Predictive Control},
author = {Amon Lahr and Joshua Näf and Kim P. Wabersich and Jonathan Frey and Pascal Siehl and Andrea Carron and Moritz Diehl and Melanie N. Zeilinger},
doi = {10.48550/arXiv.2411.19258},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
abstract = {Incorporating learning-based models, such as Gaussian processes (GPs), into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, despite recent advances in numerical optimization and real-time GP inference, its widespread application is limited by the lack of an efficient and modular open-source implementation. This work aims at filling this gap by providing an efficient implementation of zero-order Gaussian process-based MPC in acados, as well as L4acados, a general framework for incorporating non-CasADi (learning-based) residual models in acados. By providing the required sensitivities via a user-defined Python module, L4acados enables the implementation of MPC controllers with learning-based residual models in acados, while supporting custom Jacobian approximations, as well as parallelization of sensitivity computations when preparing the quadratic subproblems. The computational efficiency of L4acados is benchmarked against available software using a neural network-based control example. Last, it is used demonstrate the performance of the zero-order GP-MPC method applied to two hardware examples: autonomous miniature racing, as well as motion control of a full-scale autonomous vehicle for an ISO lane change maneuver.},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Prajapat, Manish; Lahr, Amon; Köhler, Johannes; Krause, Andreas; Zeilinger, Melanie N.
Towards Safe and Tractable Gaussian Process-Based MPC: Efficient Sampling within a Sequential Quadratic Programming Framework Proceedings Article Forthcoming
In: Forthcoming, (Accepted at the 2024 Conference on Decision and Control (CDC)).
@inproceedings{prajapat_towards_2024,
title = {Towards Safe and Tractable Gaussian Process-Based MPC: Efficient Sampling within a Sequential Quadratic Programming Framework},
author = {Manish Prajapat and Amon Lahr and Johannes Köhler and Andreas Krause and Melanie N. Zeilinger},
doi = {10.48550/arXiv.2409.08616},
year = {2024},
date = {2024-09-13},
urldate = {2024-09-13},
abstract = {Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-world applications. Yet, for computational tractability, most approaches for Gaussian process-based model predictive control (GP-MPC) are based on approximations of the reachable set that are either overly conservative or impede the controller's safety guarantees. To address these challenges, we propose a robust GP-MPC formulation that guarantees constraint satisfaction with high probability. For its tractable implementation, we propose a sampling-based GP-MPC approach that iteratively generates consistent dynamics samples from the GP within a sequential quadratic programming framework. We highlight the improved reachable set approximation compared to existing methods, as well as real-time feasible computation times, using two numerical examples.},
note = {Accepted at the 2024 Conference on Decision and Control (CDC)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Leeman, Antoine P.; Köhler, Johannes; Messerer, Florian; Lahr, Amon; Diehl, Moritz; Zeilinger, Melanie N.
Fast System Level Synthesis: Robust Model Predictive Control Using Riccati Recursions Proceedings Article
In: 8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024, IFAC-PapersOnLine, 2024.
@inproceedings{leeman_fast_2024,
title = {Fast System Level Synthesis: Robust Model Predictive Control Using Riccati Recursions},
author = {Antoine P. Leeman and Johannes Köhler and Florian Messerer and Amon Lahr and Moritz Diehl and Melanie N. Zeilinger},
url = {https://doi.org/10.48550/arXiv.2401.13762},
doi = {10.1016/j.ifacol.2024.09.027},
year = {2024},
date = {2024-09-01},
urldate = {2024-02-07},
booktitle = {8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024},
volume = {58},
number = {18},
publisher = {IFAC-PapersOnLine},
abstract = {System Level Synthesis (SLS) enables improved robust MPC formulations by allowing for joint optimization of the nominal trajectory and controller. This paper introduces a tailored algorithm for solving the corresponding disturbance feedback optimization problem. The proposed algorithm builds on a recently proposed joint optimization scheme and iterates between optimizing the controller and the nominal trajectory while converging q-linearly to an optimal solution. We show that the controller optimization can be solved through Riccati recursions leading to a horizon-length, state, and input scalability of O(N2(n3x+n3u)) for each iterate. On a numerical example, the proposed algorithm exhibits computational speedups of order 10 to 10^3 compared to general-purpose commercial solvers.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Lahr, Amon; Tronarp, Filip; Schmidt, Nathanael Bosch Jonathan; Hennig, Philipp; Zeilinger, Melanie N.
Probabilistic ODE Solvers for Integration Error-Aware Numerical Optimal Control Proceedings Article
In: Proceedings of the 6th Annual Learning for Dynamics & Control Conference (L4DC), pp. 1018–1032, PMLR, 2024.
@inproceedings{lahr_probabilistic_2024,
title = {Probabilistic ODE Solvers for Integration Error-Aware Numerical Optimal Control},
author = {Amon Lahr and Filip Tronarp and Nathanael Bosch Jonathan Schmidt and Philipp Hennig and Melanie N. Zeilinger},
url = {https://proceedings.mlr.press/v242/lahr24a.html
https://proceedings.mlr.press/v242/lahr24a/lahr24a.pdf},
year = {2024},
date = {2024-07-15},
urldate = {2024-02-07},
booktitle = {Proceedings of the 6th Annual Learning for Dynamics & Control Conference (L4DC)},
volume = {242},
pages = {1018--1032},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
abstract = {Appropriate time discretization is crucial for real-time applications of numerical optimal control, such as nonlinear model predictive control. However, if the discretization error strongly depends on the applied control input, meeting accuracy and sampling time requirements simultaneously can be challenging using classical discretization methods. In particular, neither fixed-grid nor adaptive-grid discretizations may be suitable, when they suffer from large integration error or exceed the prescribed sampling time, respectively. In this work, we take a first step at closing this gap by utilizing probabilistic numerical integrators to approximate the solution of the initial value problem, as well as the computational uncertainty associated with it, inside the optimal control problem (OCP). By taking the viewpoint of probabilistic numerics and propagating the numerical uncertainty in the cost, the OCP is reformulated such that the optimal input reduces the computational uncertainty insofar as it is beneficial for the control objective. The proposed approach is illustrated using a numerical example, and potential benefits and limitations are discussed.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Frey, Jonathan; Gao, Yunfan; Messerer, Florian; Lahr, Amon; Zeilinger, Melanie N.; Diehl, Moritz
Efficient Zero-Order Robust Optimization for Real-Time Model Predictive Control with Acados Proceedings Article
In: 2024 European Control Conference (ECC), IEEE, Stockholm, Sweden, 2024, ISBN: 978-3-9071-4410-7.
@inproceedings{frey_efficient_2023,
title = {Efficient Zero-Order Robust Optimization for Real-Time Model Predictive Control with Acados},
author = {Jonathan Frey and Yunfan Gao and Florian Messerer and Amon Lahr and Melanie N. Zeilinger and Moritz Diehl},
doi = {10.23919/ECC64448.2024.10591148},
isbn = {978-3-9071-4410-7},
year = {2024},
date = {2024-06-03},
urldate = {2023-12-18},
booktitle = {2024 European Control Conference (ECC)},
publisher = {IEEE},
address = {Stockholm, Sweden},
abstract = {Robust and stochastic optimal control problem (OCP) formulations allow a systematic treatment of uncertainty, but are typically associated with a high computational cost. The recently proposed zero-order robust optimization (zoRO) algorithm mitigates the computational cost of uncertainty-aware MPC by propagating the uncertainties separately from the nominal dynamics. This paper details the combination of zoRO with the real-time iteration (RTI) scheme and presents an efficient open-source implementation in acados, utilizing BLASFEO for the linear algebra operations. In addition to the scaling advantages posed by the zoRO algorithm, the efficient implementation drastically reduces the computational overhead, and, combined with an RTI scheme, enables the use of tube-based MPC for a wider range of applications. The flexibility, usability and effectiveness of the proposed implementation is demonstrated on two examples. On the practical example of a differential drive robot, the proposed implementation results in a tenfold reduction of computation time with respect to the previously available zoRO implementation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Lahr, Amon; Zanelli, Andrea; Carron, Andrea; Zeilinger, Melanie N.
Zero-Order Optimization for Gaussian Process-based Model Predictive Control Journal Article
In: European Journal of Control, pp. 100862, 2023, ISSN: 0947-3580.
@article{lahrZeroOrderOptimizationGaussian2023,
title = {Zero-Order Optimization for Gaussian Process-based Model Predictive Control},
author = {Amon Lahr and Andrea Zanelli and Andrea Carron and Melanie N. Zeilinger},
url = {https://www.sciencedirect.com/science/article/pii/S0947358023000912},
doi = {10.1016/j.ejcon.2023.100862},
issn = {0947-3580},
year = {2023},
date = {2023-06-15},
urldate = {2023-07-18},
journal = {European Journal of Control},
pages = {100862},
abstract = {By enabling constraint-aware online model adaptation, model predictive control using Gaussian process (GP) regression has exhibited impressive performance in real-world applications and received considerable attention in the learning-based control community. Yet, solving the resulting optimal control problem in real-time generally remains a major challenge, due to (i) the increased number of augmented states in the optimization problem, as well as (ii) computationally expensive evaluations of the posterior mean and covariance and their respective derivatives. To tackle these challenges, we employ (i) a tailored Jacobian approximation in a sequential quadratic programming (SQP) approach and combine it with (ii) a parallelizable GP inference and automatic differentiation framework. Reducing the numerical complexity with respect to the state dimension nx for each SQP iteration from O(nx6) to O(nx3), and accelerating GP evaluations on a graphical processing unit, the proposed algorithm computes suboptimal, yet feasible, solutions at drastically reduced computation times and exhibits favorable local convergence properties. Numerical experiments verify the scaling properties and investigate the runtime distribution across different parts of the algorithm.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}