What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Mathematical optimization
His main research concerns Hybrid system, Mathematical optimization, Control theory, Optimal control and Probabilistic logic.
John Lygeros has included themes like Algorithm, Reachability, Automaton and Applied mathematics in his Hybrid system study.
His studies in Mathematical optimization integrate themes in fields like Markov chain and Electric power system.
His Control theory study deals with Control intersecting with Control engineering.
The Probabilistic logic study combines topics in areas such as Robustness and Benchmark.
The various areas that John Lygeros examines in his Control theory study include Control system, Stochastic control, Fault tolerance and Model predictive control.
His most cited work include:
- Dynamical properties of hybrid automata (696 citations)
- A Survey of Applications of Wireless Sensors and Wireless Sensor Networks (625 citations)
- Controllers for reachability specifications for hybrid systems (560 citations)
What are the main themes of his work throughout his whole career to date?
John Lygeros mainly investigates Mathematical optimization, Control theory, Hybrid system, Optimal control and Model predictive control.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Probabilistic logic and Reachability.
Specifically, his work in Reachability is concerned with the study of Reachability problem.
His Control theory study combines topics in areas such as Control and Bounded function.
The study incorporates disciplines such as Control engineering, Control system, Dynamical systems theory and Algorithm in addition to Hybrid system.
His primary area of study in Optimal control is in the field of Stochastic control.
He most often published in these fields:
- Mathematical optimization (40.31%)
- Control theory (22.75%)
- Hybrid system (19.24%)
What were the highlights of his more recent work (between 2018-2021)?
- Mathematical optimization (40.31%)
- Model predictive control (11.15%)
- Optimal control (16.64%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Model predictive control, Optimal control, Control theory and Control theory.
His research is interdisciplinary, bridging the disciplines of Function and Mathematical optimization.
His Model predictive control study combines topics from a wide range of disciplines, such as Converters, Energy management, Regularization, Quadratic equation and Probabilistic logic.
John Lygeros combines subjects such as Linear system, Dynamic programming, State space, Nonlinear system and Monotonic function with his study of Optimal control.
His research integrates issues of Bayesian optimization, Bounded function and System identification in his study of Control theory.
His Control theory research is multidisciplinary, relying on both Terminal, Invariant and Topology.
Between 2018 and 2021, his most popular works were:
- Data-Enabled Predictive Control: In the Shallows of the DeePC (106 citations)
- Nash and Wardrop Equilibria in Aggregative Games With Coupling Constraints (59 citations)
- The Power of Diversity: Data-Driven Robust Predictive Control for Energy-Efficient Buildings and Districts (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Mathematical analysis
His primary areas of investigation include Mathematical optimization, Model predictive control, Optimal control, Control theory and Robustness.
His biological study spans a wide range of topics, including Function and Parallelizable manifold.
His Model predictive control research includes elements of Robust optimization, Linear system, Energy management and Bang–bang control.
His work deals with themes such as Bellman equation, Data-driven, Dynamic programming, Linear programming and Monotonic function, which intersect with Optimal control.
His research on Control theory focuses in particular on Nonlinear system.
His study in Robustness is interdisciplinary in nature, drawing from both Control engineering, Robotics and Systems architecture.
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