Amon Lahr
PhD Candidate in Mechanical and Process Engineering
Institute for Dynamic Systems and Control
ETH Zürich
https://www.youtube.com/watch?v=MXLinY7d1Yg
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.
Read more about this project
Publications
1.
Lahr, Amon; Tronarp, Filip; Schmidt, Nathanael Bosch Jonathan; Hennig, Philipp; Zeilinger, Melanie N.
Probabilistic ODE Solvers for Integration Error-Aware Model Predictive Control Working paper
2024, (Submitted to the 6th Annual Learning for Dynamics & Control Conference (L4DC 2024)).
@workingpaper{lahr_probabilistic_2024,
title = {Probabilistic ODE Solvers for Integration Error-Aware Model Predictive Control},
author = {Amon Lahr and Filip Tronarp and Nathanael Bosch Jonathan Schmidt and Philipp Hennig and Melanie N. Zeilinger},
url = {https://doi.org/10.48550/arXiv.2401.17731},
year = {2024},
date = {2024-02-07},
urldate = {2024-02-07},
abstract = {Appropriate time discretization is crucial for nonlinear model predictive control. However, in situations where 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.},
note = {Submitted to the 6th Annual Learning for Dynamics & Control Conference (L4DC 2024)},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Appropriate time discretization is crucial for nonlinear model predictive control. However, in situations where 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.
2.
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 Working paper
2024, (Submitted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)).
@workingpaper{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},
year = {2024},
date = {2024-02-07},
urldate = {2024-02-07},
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.
},
note = {Submitted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
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.
3.
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 Working paper
2023.
@workingpaper{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 = {10.48550/arXiv.2311.04557},
year = {2023},
date = {2023-12-18},
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 outside of the MPC problem. 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 = {workingpaper}
}
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 outside of the MPC problem. 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.
4.
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}
}
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.
