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Ajay Kumar Sampathirao

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Ajay Kumar Sampathirao

Technische Universität Berlin
Fachgebiet Regelungssysteme
Sekretariat EN11
Einsteinufer 17
D-10587 Berlin

Office: EN 240 (Elektrotechnik Neubau)
Phone: +49 (0)30 314-23573
Fax: +49 (0)30 314-21137
Email: Sampathirao@control.TU-berlin.de

Biography Hey, I'm Ajay and I am from India. I received my bachelors in electrical and electronics engineering from NIT Warangal, 2010. In 2012, I received my masters from IIT Delhi with specialisation in control and automation. I worked as a software engineer for a brief period in WESEE (Weapons and Electronics Systems Engineering) where I worked on numerical platforms estimation of actual signal from corrupt measurements. In 2013, I joined in the doctoral program in computer, decision systems unit in IMT, Lucca, Italy. My supervisors are Alberto Bemporad and Pantelis Sopasakis and discussed my thesis in December 2016.

Research My research focus on Model Predictive Control (MPC), parallel optimisation methods and GPUs. My PhD title is "Parallel methods of solving stochastic optimal control: control for drinking water networks". This work developed algorithms that can be suitably parallised to solve stochastic optimal control inn real-time. These algorithms are implemented on GPU hardware and employed for economic management of water networks.

Current work Currently, I am working on centralised and distributed energy management system (EMS) for isolated and interconnected microgrid. The objective of the EMS solutions is to maximise the infeed from the renewable sources and minimise the operation of the thermal and diesel generators.


Ajay Kumar Sampathirao, Grosso, Juan Manuel, Pantelis Sopasakis, Ocampo-Martinez, Carlos, Alberto Bemporad, Vicenc Puig. Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study. In 19th IFAC World Congress, Cape Town, South Africa, 2014.

Sampathirao, A.K., Sopasakis, P., Bemporad, A.. Decentralised hierarchical multi-rate control of large-scale drinking water networks. In 9th Int. Conf. Critical Information Infrastructures Security, volume 8985 of Lecture Notes in Computer Science (LNCS), Limassol, Cyprus, October 2014.

Sampathirao, A.K., Sopasakis, P., Bemporad, A., Patrinos, P.. Distributed solution of stochastic optimal control problems on GPUs. In 54 IEEE Conf. Decision and Control, Osaka, Japan, Dec 2015.

Sampathirao, A.K., Sopasakis, P., Bemporad, A., Patrinos, P.. Fast parallelizable scenario-based stochastic optimization. In 4th European Conference on Computational Optimization, Leuven, Belgium, September 2016.

A. K. Sampathirao, P. Sopasakis, A. Bemporad, P. Patrinos. GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks. IEEE Transactions on Control Systems Technology, PP (99):1-12, 2017.
Ajay Kumar Sampathirao, Pantelis Sopasakis, Alberto Bemporad, Panagiotis Patrinos. Proximal Limited-Memory Quasi-Newton Methods for Scenario-based Stochastic Optimal Control. In volume 50 pages 11865 - 11870, 2017. 20th IFAC World Congress.
P. Sopasakis, A.K. Sampathirao, A. Bemporad, P. Patrinos. Uncertainty-aware demand management of water distribution networks in deregulated energy markets. Environmental Modelling & Software, 101 pages 10 - 22, 2018.

Working paper

  • A.K. Sampathirao, P. Sopasakis, A. Bemporad and P. Patrinos, “Quasi-Newton scenario-based stochastic optimal control problems”
  • A. Schödl, C.A. Hans, A.K. Sampathirao, J. Raisch, “Distributed Model Predictive Secondary Voltage Control of Islanded Microgrids”


Ajay Kumar Sampathirao. Parallel methods for solving stochastic optimal control : control of drinking water networks. IMT, Dec 2016.

Open thesis

Master thesis: "Distributed Energy Management system for operational control of Microgrid."
A microgrid (MG) is a small-scale power system that clusters and manages renewable energy resources and loads within a defined geographical boundary. Microgrid has emerged as a viable architecture to tackle the challenges with high infeed of renewable energy in the future power systems. Energy management system (EMS) in the MG manages the operation control and balancing the demand with the generation. Typical EMS designs are centralised with a single unit in-charge of decision making. This approach lacks scalability, privacy and sensitive to single-point failures. This objective of this thesis is to design of a distributed EMS for operation of MG. [1]

Interested students can send an email or come to the office to discuss more about this project.

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