Research ZeilingerOpen OpportunitiesMost control methods operate under the assumption of a known model. However, in practice, knowing the exact dynamics model a priori is unrealistic. A common approach is to model the unknown dynamics using Gaussian Processes (GPs) which can characterize uncertainty and formulate a Model Predictive Control (MPC) type problem. However, it is difficult to exactly utilize this uncertainty characterization in predictive control.
In a recent approach [1], we proposed a sampling-based robust GP-MPC formulation for accurate uncertainty propagation by sampling continuous functions. In contrast, in the proposed project, you will implement an approximation method for sampling continuous functions using a finite number of basis functions [2] and solve the MPC problem jointly with the sampled dynamics. You will analyze the trade-offs between performance, approximation accuracy, and computational cost for this method. - Engineering and Technology
- Master Thesis, Semester Project
| In this semester thesis, our goal is to enable an F1Tenth car, an autonomous vehicle at 1:10 scale of a Formula 1 car, to race safely on a track that is perceived through RGB-D images captured by an onboard camera. - Engineering and Technology
- Semester Project
| This project aims to improve the design of predictive controllers that robustly ensure safe operation for a large class of uncertain nonlinear systems. - Dynamical Systems, Systems Theory and Control
- ETH Zurich (ETHZ), Master Thesis
| Hydrocephalus is a medical condition characterized by the disturbed dynamics of cerebrospinal fluid (CSF) and its excessive accumulation in the brain ventricles. In contemporary therapy, a shunt system is implanted that drains CSF from the ventricles into the peritoneal space. While various types of shunt systems exist, they are essentially all based on passive mechanical pressure valves that are driven by the external pressure gradient. This limits the efficacy of these shunts and complications such as over- and underdrainage may occur. To improve the therapy of hydrocephalus, we are working towards intelligent mechatronic shunt systems that are capable of monitoring vital signs and adapting CSF drainage according to the patient’s actual needs. In this project, you will support the technical upgrade of an existing hardware-in-the-loop test bench that is used for the evaluation of existing shunt systems and the development of smart shunt system. - Biomedical Engineering, Dynamical Systems, Electrical Engineering, Mechanical Engineering, Systems Theory and Control
- Student Assistant / HiWi
| This project aims to develop an online learning framework for achieving precise position control of a soft robotic arm while adapting to time-varying system dynamics. - Engineering and Technology
- Master Thesis
| MOTIVATION ⇾ Creating a digital twin of the robot's environment is crucial for several reasons:
1. Simulate Different Robots: Test various robots in a virtual environment, saving time and resources.
2. Accurate Evaluation: Precisely assess robot interactions and performance.
3. Enhanced Flexibility: Easily modify scenarios to develop robust systems.
4. Cost Efficiency: Reduce costs by identifying issues in virtual simulations.
5. Scalability: Replicate multiple environments for comprehensive testing.
PROPOSAL
We propose to create a digital twin of our Semantic environment, designed in your preferred graphics Platform to be able to simulate Reinforcement Learning agents in the digital environment, to create a unified evaluation platform for robotic tasks. - Artificial Intelligence and Signal and Image Processing
- Master Thesis, Semester Project
| The goal of the project consists in deriving error bounds for the approximate Gaussian process regression method given by the FITC method. - Engineering and Technology
- Master Thesis, Semester Project
| Multi-task learning is the problem of jointly learning multiple functions
that are “related” to each other. By leveraging this similarity, estimation performance can be
improved on each (possibly unseen) task, and one can make an efficient use of the available
data. The project aims at deriving
uncertainty bounds around the multi-task-system estimates. Specifically, the candidate will
work with the regularized trigonometric regression inspired by the so-called sparse-spectrum
Gaussian process regression, investigate the issue of bias learning (i.e., finding the features
that encode similarity among tasks) and derive error bounds for it, possibly setting the analysis
in the statistical learning framework. - Engineering and Technology
- Master Thesis, Semester Project
| One of the key ingredients in Model Predictive Control (MPC) schemes
is an effective model of the dynamical system’s response to external inputs. However, first-
principles models are often not accurate enough, as there might be unknown external disturbances and model mismatches. To address this, learning-based control aims at
complementing nominal models with data-based ones, which can be refined online as new system
observations are gathered. Thus, such a model should be both expressive and fast to update.
This project focuses on a learning-based stochastic
MPC scheme, where uncertainty in the model is learned
with an approximate Gaussian process, namely the regularized trigonometric regression stemming from the so-
called sparse-spectrum Gaussian processes. To this
aim, the candidate will review the available uncertainty
bounds around these approximate Gaussian-process-based estimates and incorporate them in the
MPC formulation. The chance-constraints thereby obtained are then to be analyzed to rigorously prove recursive feasibility and stability of the closed-loop system. - Engineering and Technology
- Master Thesis, Semester Project
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