What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mathematical analysis
Frank Allgöwer mainly focuses on Control theory, Model predictive control, Nonlinear system, Mathematical optimization and Stability.
His Control theory study frequently draws connections between related disciplines such as Bounded function.
His Model predictive control research includes themes of Control engineering, Nonlinear control, Exponential stability and Optimal control.
His Nonlinear system research integrates issues from Observer and Process.
His biological study spans a wide range of topics, including Graph and Adaptive control.
His Stability study integrates concerns from other disciplines, such as Range and Control system.
His most cited work include:
- A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability (1113 citations)
- Brief paper: An internal model principle is necessary and sufficient for linear output synchronization (659 citations)
- Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations (549 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Control theory, Mathematical optimization, Nonlinear system, Model predictive control and Control theory.
Linear system, Stability, Robustness, Exponential stability and Control system are among the areas of Control theory where the researcher is concentrating his efforts.
His work on Optimization problem and Optimal control as part of general Mathematical optimization study is frequently linked to Convex optimization and Set, therefore connecting diverse disciplines of science.
His studies in Nonlinear system integrate themes in fields like Observer, Trajectory and Applied mathematics.
His Model predictive control research is multidisciplinary, relying on both Nonlinear model and Robust control.
His Control theory study results in a more complete grasp of Control engineering.
He most often published in these fields:
- Control theory (61.61%)
- Mathematical optimization (28.64%)
- Nonlinear system (28.04%)
What were the highlights of his more recent work (between 2017-2021)?
- Control theory (61.61%)
- Model predictive control (25.39%)
- Nonlinear system (28.04%)
In recent papers he was focusing on the following fields of study:
Frank Allgöwer mainly investigates Control theory, Model predictive control, Nonlinear system, Mathematical optimization and Stability.
His is doing research in Exponential stability, Trajectory, Control theory, Control system and Linear system, both of which are found in Control theory.
His work in Model predictive control covers topics such as Optimization problem which are related to areas like Resource.
The various areas that Frank Allgöwer examines in his Nonlinear system study include Data-driven, Applied mathematics, Polynomial and Robustness.
His Mathematical optimization research is multidisciplinary, incorporating elements of Computation and Dual.
In his study, Bounded function is strongly linked to Robust control, which falls under the umbrella field of Stability.
Between 2017 and 2021, his most popular works were:
- Data-Driven Model Predictive Control With Stability and Robustness Guarantees (65 citations)
- Learning an Approximate Model Predictive Controller With Guarantees (64 citations)
- Robust MPC with recursive model update (52 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Artificial intelligence
- Mathematical analysis
Frank Allgöwer focuses on Control theory, Model predictive control, Mathematical optimization, Trajectory and Nonlinear system.
By researching both Control theory and Terminal, Frank Allgöwer produces research that crosses academic boundaries.
His Model predictive control research incorporates elements of Stability, Optimization problem and Benchmark.
His study explores the link between Stability and topics such as Noise that cross with problems in Robust control and State.
The Mathematical optimization study combines topics in areas such as Control and Dynamic priority scheduling.
His study in Trajectory is interdisciplinary in nature, drawing from both Data-driven and Optimal control.
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.
