What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Operating system
Elias B. Kosmatopoulos mainly focuses on Control theory, Adaptive control, Simulation, Stability and Control system.
His research in Control theory intersects with topics in Estimation theory and Bounded function.
Adaptive control is a subfield of Control theory that Elias B. Kosmatopoulos studies.
His Simulation research is multidisciplinary, incorporating perspectives in Distributed generation, Energy management system, Energy management, Schedule and Reliability engineering.
His study focuses on the intersection of Lyapunov stability and fields such as Traffic simulation with connections in the field of Control engineering.
In his work, Artificial intelligence is strongly intertwined with Transfer function, which is a subfield of Dynamical systems theory.
His most cited work include:
- High-order neural network structures for identification of dynamical systems (676 citations)
- A roadmap towards intelligent net zero- and positive-energy buildings (250 citations)
- Store-and-forward based methods for the signal control problem in large-scale congested urban road networks (219 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Control theory, Adaptive control, Control engineering, Nonlinear system and Mathematical optimization.
Control theory and Artificial neural network are frequently intertwined in his study.
His Adaptive control study combines topics from a wide range of disciplines, such as Feedback linearization, Robust control, Sliding mode control, Bounded function and Control-Lyapunov function.
His research on Control engineering also deals with topics like
- Adaptive optimization together with Distributed computing, Robot and Simulation,
- Mobile robot and related Real-time computing.
His Mathematical optimization study incorporates themes from Control and Stochastic approximation.
His Control research incorporates themes from Operations research, Systems engineering and Traffic congestion.
He most often published in these fields:
- Control theory (32.66%)
- Adaptive control (21.61%)
- Control engineering (21.11%)
What were the highlights of his more recent work (between 2017-2021)?
- Control theory (32.66%)
- Distributed computing (8.54%)
- Robot (15.58%)
In recent papers he was focusing on the following fields of study:
Elias B. Kosmatopoulos mainly investigates Control theory, Distributed computing, Robot, Adaptive optimization and Real-time computing.
Adaptive control and Lyapunov function are the primary areas of interest in his Control theory study.
His Adaptive control study is related to the wider topic of Control theory.
Elias B. Kosmatopoulos interconnects Human-in-the-loop, Distributed generation and Exploit in the investigation of issues within Distributed computing.
His work on Cognitive robotics as part of general Robot research is often related to A priori and a posteriori, thus linking different fields of science.
His Adaptive optimization research also works with subjects such as
- Metric together with Resilience,
- Unavailability which connect with Mathematical optimization and Linear programming.
Between 2017 and 2021, his most popular works were:
- An Adaptive Learning-Based Approach for Nearly Optimal Dynamic Charging of Electric Vehicle Fleets (29 citations)
- Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study (27 citations)
- Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Control theory
Elias B. Kosmatopoulos spends much of his time researching Distributed computing, Robot, Control engineering, Control theory and Optimal control.
His Distributed computing research includes elements of Simulation and Occupancy.
His work on Cognitive robotics as part of general Robot study is frequently connected to A priori and a posteriori, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The study incorporates disciplines such as Adaptive optimization, Electric vehicle, Dynamic programming, Adaptive learning and Scheduling in addition to Control engineering.
His Lyapunov function study in the realm of Control theory interacts with subjects such as Dwell time.
His biological study spans a wide range of topics, including Optimization problem, Self-tuning and Zoning.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
