M.Sc. Juan Pablo Zometa
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Ph.D. Student and Research Assistant
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Phone: |
(+49)0391/6712430 |
Fax: |
(+49)0391/6711191 |
Email: |
My main research interest is the implementation of online model predictive control (MPC) schemes
for embedded applications. In particular, I work on MPC on embedded platforms controlling
mechatronic systems.
I am the main developer of MPC code generation tool μAO-MPC.
Efficient Stochastic Model Predictive Control for Embedded Systems Based on Second-order Cone Programs (to appear - ECC 2016).
Efficiency and Performance of Embedded Model Predictive Control for Active Vibration Attenuation (to appear - ECC 2016).
Predictive Control, Embedded Cyberphysical Systems and Systems of Systems - A Perspective (to appear - ARC 2016).
Efficient Stochastic Model Predictive Control based on Polynomial Chaos Expansions for Embedded Applications. (CDC 2015).
Predictive Control in the Era of Networked Control and Communication – a Perspective. (NMPC 2015).
Predictive Path-following Control: Concept and Implementation for an Industrial Robot (MSC 2013).
Modeling, Parameter Identification and Model-Based Control of a Robotic Manipulator (MSC 2013). Related information can be found here.
muAO-MPC: A Free Code Generation Tool for Embedded Real-Time Linear Model Predictive Control (ACC 2013 ). Related information can be found here.
On Tailored Model Predictive Control for Low Cost Embedded Systems with Memory and Computational Power Constraints - (technical report, .pdf)
Implementation Aspects of Model Predictive Control for Embedded Systems (ACC 2012). Related information can be found here. Go to Top
If you are interested in the implementation of linear or nonlinear controllers for fast mechatronic systems (robotics, embedded...)
feel free to contact me. But before you do that, here are a few hints that may increase your chances of being considered as a candidate:
First check
the list of projects I am responsible for.
If you find any of those projects interesting, then send me a short e-mail with the following information:
- a brief description of who you are: name, study program, time planned for developing the project,
- which project are you interested in,
- a clear statement of why you think you could succesfully complete the project, and to backup this claim
attach the following two files as PDF:
-- A one-page curriculum vitae with the information that is relevant to the project (do not include hobbies or primary school info, for example).
-- A transcript of your grades from Bachelor and Master studies.
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μAO-MPC: A model predictive control software tool for linear time-invariant embedded applications.
KUKA LWR IV dynamic model library: a C/Python software library of the dynamic model of a KUKA Lightweight Robot IV (LWR IV).
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