Jonas G. Matt

I am a first year PhD candidate in Florian Dörfler’s group at the Automatic Control Laboratory (IfA) of ETH Zürich. My other mentors are Saverio Bolognani and Giuseppe Belgioioso.

In my research, I strive to make the systems and infrastructure we use everyday smarter, more efficient, and more sustainable. I believe that control theory, machine learning, and data science are the tools that will help us achieve that.

My previous work has ranged from modeling the physics of soft robots to real-time voltage control in power networks. Next to academia, I have obtained professional experience in data analytics, renewable energy systems, and Internet of Things technologies.

Presently, my research focuses on the future proofness of energy systems. By coordinating the interactions between the entities that operate different levels of power grids, I aim to maximize the amount of electricity that can be safely transferred across the network. Among other things, this enables a faster integration of renewable energy sources and, thus, supports the ongoing energy transition. If you would like to learn more about my work and/or are looking for open projects, have a look at the MAESTRO project.

news

Sep 11, 2025	I presented my work done with ABB on the Lossless Compression of Time Series Data at the IEEE ETFA 2025 conference in Porto. If you are curious about how to best compress your time series, check out the paper.
Sep 04, 2025	My poster on Incentive Design for Procuring Control Services received the Community Best Paper Award at the Swiss CLOCK Summit 2025! Check it out here: pdf

selected publications

preprint

Data-Driven Soft Robot Control via Adiabatic Spectral Submanifolds

Roshan S. Kaundinya, John Irvin Alora, Jonas G. Matt, and 3 more authors

Mar 2025

Abs arXiv

The mechanical complexity of soft robots creates significant challenges for their model-based control. Specifically, linear data-driven models have struggled to control soft robots on complex, spatially extended paths that explore regions with significant nonlinear behavior. To account for these nonlinearities, we develop here a model-predictive control strategy based on the recent theory of adiabatic spectral submanifolds (aSSMs). This theory is applicable because the internal vibrations of heavily overdamped robots decay at a speed that is much faster than the desired speed of the robot along its intended path. In that case, low-dimensional attracting invariant manifolds (aSSMs) emanate from the path and carry the dominant dynamics of the robot. Aided by this recent theory, we devise an aSSM-based model-predictive control scheme purely from data. We demonstrate the effectiveness of this data-driven model on various dynamic trajectory tracking tasks on a high-fidelity and high-dimensional finite-element model of a soft trunk robot. Notably, we find that four- or five-dimensional aSSM-reduced models outperform the tracking performance of other data-driven modeling methods by a factor up to 10 across all closed-loop control tasks.
conference

Lossless Compression of Time Series Data: A Comparative Study

Jonas G. Matt, Pengcheng Huang, and Balz Maag

In 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2025

Abs arXiv HTML

Our increasingly digital and connected world has led to the generation of unprecedented amounts of data. This data must be efficiently managed, transmitted, and stored to preserve resources and allow scalability. Data compression has therein been a key technology for a long time, resulting in a vast landscape of available techniques. This largest-to-date study analyzes and compares various lossless data compression methods for time series data. We present a unified framework encompassing two stages: data transformation and entropy encoding. We evaluate compression algorithms across both synthetic and real-world datasets with varying characteristics. Through ablation studies at each compression stage, we isolate the impact of individual components on overall compression performance–revealing the strengths and weaknesses of different algorithms when facing diverse time series properties. Our study underscores the importance of well-configured and complete compression pipelines beyond individual components or algorithms; it offers a comprehensive guide for selecting and composing the most appropriate compression algorithms tailored to specific datasets.