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
| 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
| 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
|
|
