DR-IRMPC
DR-IRMPC is a risk-constrained infinite-horizon optimal control framework to solve these class of problems in an iterative manner.
Posts by Collection
portfolio
PyCM
PyCM is a multi-class confusion matrix library written in Python that is a proper tool for post-classification model evaluation.
Pymilo
Pymilo is an open source Python package to export pre-trained machine learning models in a transparent way.
Nava
Nava is a Python library that allows users to play sound in Python without any dependencies or platform restrictions.
publications
PyCM: Multiclass confusion matrix library in Python
Published in Journal of Open Source Software, 2018
PyCM is a multi-class confusion matrix library written in Python that is a proper tool for post-classification model evaluation.
Recommended citation: S. Haghighi, M. Jasemi, S. Hessabi, and A. Zolanvari, “PyCM: Multiclass confusion matrix library in Python,” Journal of Open Source Software, vol. 3, no. 25, p. 729, 2018. https://joss.theoj.org/papers/10.21105/joss.00729.pdf
A Q-learning approach for controlling a robotic goalkeeper during penalty procedure
Published in 2nd International Congress on Science and Engineering, 2019
This study was aimed to solve the problem of penalty kick goalkeeping for RoboCup Small Size soccer robots using a Q-learning approach.
Recommended citation: A. Zolanvari, M. Shirazi and M. Menhaj, "A q-learning approach for controlling a robotic goalkeeper during penalty procedure," in II International Congress on Science and Engineering, Hamburg-Germany, 2019. https://shorturl.at/mBEOZ
Data-driven distributionally robust iterative risk-constrained model predictive control
Published in 2022 European Control Conference (ECC), 2022
This paper considers a risk-constrained infinite-horizon optimal control problem and proposes to solve it in an iterative manner. (ECC 2022 Best Student Paper Award Finalist)
Recommended citation: A. Zolanvari and A. Cherukuri, "Data-driven distributionally robust iterative risk-constrained model predictive control," in 2022 European Control Conference (ECC), 2022. https://ieeexplore.ieee.org/abstract/document/9838319
Data-driven distributionally robust optimization over a network via distributed semi-infinite programming
Published in IEEE 61st Conference on Decision and Control (CDC), 2022
This paper focuses on solving a data-driven distributionally robust optimization problem over a network of agents.
Recommended citation: A. Cherukuri, A. Zolanvari, G. Banjac and A. R. Hota, "Data-driven distributionally robust optimization over a network via distributed semi-infinite programming," in 2022 IEEE 61st Conference on Decision and Control (CDC), 2022. https://ieeexplore.ieee.org/abstract/document/9992604
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.