Michael Muehlebach

I am currently a postdoctoral student at the University of California, Berkeley, supervised by Prof. Michael I. Jordan.

I studied mechanical engineering at ETH Zurich and specialized in robotics, systems, and control during my Master's degree. I received the B.Sc. and the M.Sc. in 2010 and 2013, respectively, before joining the Institute for Dynamic Systems and Control for my Ph.D. I graduated under the supervision of Prof. R. D'Andrea in 2018.

I am interested in a wide variety of subjects, including dynamics, control, machine learning, and optimization. During my Ph.D. I worked on approximations of the constrained linear quadratic regulator problem with applications to model predictive control. I also designed control and estimation algorithms for a balancing robot and a flying machine.

I received the Outstanding D-MAVT Bachelor Award, the Willi-Studer prize for the best Master's degree, and the ETH Medal and the HILTI prize for my doctoral thesis. I am a Branco Weiss Fellow since 2018.

Contact

Adress: Soda Hall 517, UC Berkeley, USA

E-Mail: michaelm@berkeley.edu

Publications

Journal Publications

M. Muehlebach and R. D'Andrea, "A Method for Reducing the Complexity of Model Predictive Control in Robotics Applications", IEEE Robotics and Automation Letters, 2019, https://arxiv.org/abs/1903.07648

M. Muehlebach and R. D'Andrea, "Accelerometer-Based Tilt Determination for Rigid Bodies with a Non-Accelerated Pivot Point", IEEE Transactions on Control Systems Technology, 2018

M. Muehlebach and S. Trimpe, "Distributed Event-Based State Estimation for Networked Systems: An LMI-Approach", IEEE Transactions on Automatic Control, 2017

M. Muehlebach and R. D'Andrea, "The Flying Platform - A Testbed for Ducted Fan Actuation and Control Design", Mechatronics, 2017

M. Muehlebach and R. D'Andrea, "Nonlinear Analysis and Control of a Reaction Wheel-based 3-D Inverted Pendulum", IEEE Transactions on Control Systems Technology, 2016

M. Muehlebach, T. Heimsch, and Ch. Glocker, "Variational Integrators - A Continuous Time Approach", International Journal for Numerical Methods in Engineering, 2016

H. Maes, G. Vandersteen, M. Muehlebach, and C. Ionescu, "A Fan-based Low-frequent Forced Oscillation Technique Apparatus", IEEE Transactions on Instrumentation and Measurements, 2014


Conference Publications

M. Muehlebach and M. I. Jordan, "Continuous-time Lower Bounds for Gradient-based Algorithms", Proceedings of the International Conference on Machine Learning, 2020, https://arxiv.org/abs/2002.03546

M. Muehlebach and M. I. Jordan, "A Dynamical Systems Perspective on Nesterov Acceleration", Proceedings of the International Conference on Machine Learning, 2019, https://arxiv.org/abs/1905.07436

N. B. Erichson, M. Muehlebach, and M. Mahoney, "Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction", Machine Learning and the Physical Sciences Workshop, Conference on Neural Information Processing Systems, 2019, https://arxiv.org/abs/1905.10866

M. Muehlebach and R. D'Andrea, "Basis Functions Design for the Approximation of Constrained Linear Quadratic Regulator Problems Encountered in Model Predictive Control", Proceedings of the International Conference on Decision and Control, 2017

C. Sferrazza, M. Muehlebach, and R. D'Andrea, "Trajectory Tracking and Iterative Learning on an Unmanned Aerial Vehicle using Parametrized Model Predictive Control", Proceedings of the International Conference on Decision and Control, 2017

M. Muehlebach, C. Sferrazza, and R. D'Andrea, "Implementation of a Parametrized Infinite-Horizon Model Predictive Control Scheme with Stability Guarantees", Proceedings of the International Conference on Robotics and Automation, 2017

M. Muehlebach and R. D'Andrea, "Approximation of Continuous-Time Infinite-Horizon Optimal Control Problems Arising in Model Predictive Control", Proceedings of the International Conference on Decision and Control, 2016

M. Muehlebach and R. D'Andrea, "Parametrized Infinite-horizon Model Predictive Control for Linear Time-invariant Systems with Input and State Constraints", Proceedings of the American Control Conference, 2016

M. Hofer, M. Muehlebach, and R. D'Andrea, "Application of an Approximate Model Predictive Control Scheme on an Unmanned Aerial Vehicle", Proceedings of the Conference on Robotics and Automation, 2016

M. Muehlebach and S. Trimpe, "LMI-based Synthesis for Distributed Event-based State Estimation", Proceedings of the American Control Conference, 2015

M. Muehlebach and S. Trimpe, "Guaranteed H2 Performance in Distributed Event-based State Estimation", Proceedings of the Conference on Event-based Control, Communication, and Signal Processing, 2015

M. Muehlebach, Gajamohan M., and R. D'Andrea, "Nonlinear Analysis and Control of a Reaction Wheel-based 3D Inverted Pendulum", Proceedings of the International Conference on Decision and Control, 2013

M. Gajamohan, M. Muehlebach, T. Widmer, and R. D'Andrea, "The Cubli: A Reaction Wheel-based 3D Inverted Pendulum", Proceedings of the European Control Conference, 2013


Preprints

M. Muehlebach and M. I. Jordan, "Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives", submitted to the Journal of Machine Learning Research, 2020, https://arxiv.org/abs/2002.12493


Technical Reports

M. Muehlebach, "The Silver Ratio and its Relation to Controllability", 2019, https://arxiv.org/abs/1908.07109

M. Muehlebach and R. D'Andrea, "On the Approximation of Constrained Linear Quadratic Regulator Problems and their Application to Model Predictive Control", 2018, https://doi.org/10.3929/ethz-b-000292793


Videos from past projects

The Flying Platform was designed to study ducted fan actuation. It was also used for benchmarking novel control strategies that account for actuation limits. Control algorithms explicitly accounting for these limitation can provide larger stability margins and other performance enhancements.

I supervised Julien Kohler's Master thesis, where we designed control, estimation, and learning algorithms for aggressive quadrotor maneuvers.

The Cubli is a balancing robot that can balance on its corner and jump up. I investigated the dynamics, and implemented and tested a nonlinear controller. I also worked on the learning algorithm that enables the system to adapt to a changing environment.