Event Location
3540 Engineering

Mechanical Engineering Faculty Candidate

Tuesday, April 17, 2018

10:15 a.m., 3540 Engineering Building

Refreshments Served at 10:00 a.m.

Faculty Round Table Discussion

Room 2555D Engineering Building

Leveraging nonlinear dynamics and data-driven control towards higher energy efficiency

Ashkan Haji Hosseinloo

Ph.D. Candidate




As the complexity and connectivity of the structures and the machines we build increase, conventional analysis, design, and control methods become less effective. In my research I leverage nonlinear dynamics and data-driven controls for analysis and control of complex dynamical systems ranging from vibratory energy harvesters and smart buildings to autonomous vehicles and robotics. In this talk I will present how inclusion of nonlinearity and data-driven controls in presence and absence of an accurate model, respectively, could be employed towards higher technology efficiency. In the first part of the talk I focus on the fundamental power limits of nonlinear vibration energy harvesting and techniques for approaching these limits. To this end, I will first present a generic model-based framework for calculation of the power limits. As a byproduct of this analysis, I then characterize a universal nonlinear optimal law, termed the Buy-Low-Sell-High (BLSH) strategy that maximizes the harvested power. I will conclude the first part of the talk by proposing three mechanisms to implement this strategy: latch-assisted harvesters, adaptive bistable harvesters, and harvesting via structural instabilities.

In the second part of the talk I will present my recent work on data-driven and autonomous climate control of next-generation smart buildings to concurrently improve occupants’ comfort and energy efficiency. Buildings account for nearly 40% of the total energy consumption in the United States, about half of which is used by the HVAC systems. Traditional rule-based and model-based strategies for the control of HVAC systems are often far from optimal due to the complexity of building thermal dynamics and stochasticity in the environment. To remedy this issue, I employ reinforcement learning techniques which are tailored to find adaptive and more efficient control policies while relying only on a small data-set. The general ideas and techniques presented here are applicable to other areas of research including autonomous vehicles, robotics, and predictive maintenance.  

About the Speaker

Ashkan Haji Hosseinloo is a Ph.D. candidate at MIT in the department of Mechanical Engineering where he works with Konstantin Turitsyn on nonlinear vibration energy harvesting and data-driven climate control in buildings. Ashkan’s research is in the field of dynamical systems and controls with a focus on structural dynamics and vibration, energy harvesting, vehicle dynamics, and machine learning. He holds a Master’s degree in Mechanical Engineering from NTU of Singapore where he worked on a number of applied industrial projects in dynamics and vibration in collaboration with ST Kinetics, Defense Science Organization (DSO) and SMRT Corporation of Singapore. Ashkan is the recipient of a number of awards including Den Hartog Award in Mechanics, Martin Fellowship in Design, and Graduate Exploration Fellowship from MIT and ST Kinetics and DSO scholarships from Singapore.