What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Electrical engineering
Rolf Findeisen spends much of his time researching Model predictive control, Control theory, Nonlinear system, Control engineering and Mathematical optimization.
The study incorporates disciplines such as Control theory, Linear system, Nonlinear model and Optimal control in addition to Model predictive control.
Stability, Observer, Discrete time and continuous time, Control system and Robust control are the core of his Control theory study.
His Nonlinear system study combines topics from a wide range of disciplines, such as Linear programming and Separable space.
His research integrates issues of Robot, Control, Actuator and Linear model predictive control in his study of Control engineering.
His work on Semidefinite programming as part of general Mathematical optimization research is often related to Mathematical proof, thus linking different fields of science.
His most cited work include:
- Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations (549 citations)
- Robust output feedback model predictive control of constrained linear systems (331 citations)
- State and output feedback nonlinear model predictive control: An overview (284 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Control theory, Model predictive control, Mathematical optimization, Nonlinear system and Control theory.
Rolf Findeisen regularly links together related areas like Control in his Control theory studies.
His Model predictive control course of study focuses on Nonlinear model and Fractionating column.
Rolf Findeisen has included themes like Estimation theory, Bounded function, Robust control and System dynamics in his Mathematical optimization study.
His study in Nonlinear system is interdisciplinary in nature, drawing from both State, Actuator, Linear programming, Observability and Trajectory.
His studies deal with areas such as Control system, Constraint satisfaction and State as well as Stability.
He most often published in these fields:
- Control theory (59.83%)
- Model predictive control (45.43%)
- Mathematical optimization (25.76%)
What were the highlights of his more recent work (between 2018-2021)?
- Control theory (59.83%)
- Model predictive control (45.43%)
- Control theory (18.84%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Control theory, Model predictive control, Control theory, Gaussian process and Electric power system.
His Optimal control, Robustness, Nonlinear system and Actuator study, which is part of a larger body of work in Control theory, is frequently linked to Constant, bridging the gap between disciplines.
His work carried out in the field of Nonlinear system brings together such families of science as Dynamical systems theory and Bounded function.
The various areas that Rolf Findeisen examines in his Model predictive control study include Stability, Robot, Constraint satisfaction and Mathematical optimization.
His Mathematical optimization research is multidisciplinary, relying on both Flux balance analysis and If and only if.
He has researched Control theory in several fields, including Quadcopter and Obstacle avoidance.
Between 2018 and 2021, his most popular works were:
- Response to IL-6 trans- and IL-6 classic signalling is determined by the ratio of the IL-6 receptor α to gp130 expression: fusing experimental insights and dynamic modelling (24 citations)
- Investigation of the low frequency Warburg impedance of Li-ion cells by frequency domain measurements (18 citations)
- Quantitative imaging of electric surface potentials with single-atom sensitivity. (17 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Artificial intelligence
- Electrical engineering
Control theory, Model predictive control, Control theory, Optimal control and Robustness are his primary areas of study.
His Control theory study frequently intersects with other fields, such as State.
His Model predictive control research is multidisciplinary, incorporating perspectives in Robot and Biochemical engineering.
His Control theory study integrates concerns from other disciplines, such as Control system, Quadcopter, Transmission and Stability.
His studies in Quadcopter integrate themes in fields like Control engineering, Hierarchical database model, Obstacle avoidance and Integer programming.
His Optimal control study is focused on Mathematical optimization in general.
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.
