CV
Education
- B.S. in Electromechanical Engineering, National University of Central Buenos Aires, 2015
- Ph.D in Engineering, National University of Central Buenos Aires, 2019
Work experience
- 2021-Current: Research Assistant
- The University of Edinburgh
- Duties included: Research, supervising students
- Supervisor: Dr. Stefano V. Albrecht
- 2020-2021: Research Assistant
- Louisiana State University
- Duties included: Research, teaching, supervising students
- Supervisor: Dr. Corina Barbalata
- 2015-2020: PhD Researcher
- National University of Central Buenos Aires
- Duties included: Research
- Supervisor: Dr. Gerardo G. Acosta
Teaching
Selected Publications
An adaptive data-driven controller for underwater manipulators with variable payload
Carlucho Ignacio, Dylan W. Stephens, Corina Barbalata. (2021). "An adaptive data-driven controller for underwater manipulators with variable payload." Applied Ocean Research .
MPPT for PV systems using deep reinforcement learning algorithms
Avila L., De Paula, M., Carlucho, I., Sanchez Reinoso, C. (2019). " MPPT for PV systems using deep reinforcement learning algorithms." IEEE Latin America Transactions .
An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots
Carlucho, I., De Paula, M., Acosta, G.G. (2020). " An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots." ISA Transactions .
Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids
Avila L., De Paula, M., Trimboli, M., Carlucho, I. (2020). " Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids ." Applied Soft Computing .
Double Q-PID algorithm for mobile robot control
Carlucho, I., De Paula, M., Acosta, G.G. (2019). " Double Q-PID algorithm for mobile robot control." Expert Systems with Applications .
Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning
Carlucho, I., De Paula, M., Wang S., Petillot Y.R. Acosta, G.G. (2018). " Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning." Robotics and Autonomous Systems.
Incremental Q-learning strategy for adaptive PID control of mobile robots”
Carlucho, I., De Paula, M., Villar, S.A., Acosta, G.G.. (2017). " Incremental Q-learning strategy for adaptive PID control of mobile robots." Expert Systems with Applications . 80, pp. 183-199