What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mathematical optimization
Control theory, Mathematical optimization, Model predictive control, Optimal control and Nonlinear system are his primary areas of study.
His work deals with themes such as Robot kinematics, Iterative method, Computation and Motion control, which intersect with Control theory.
His Mathematical optimization research incorporates elements of Convex function, Nonlinear programming, Robustness and Convex optimization.
His Model predictive control research incorporates themes from Nonlinear control, Control engineering, Algorithm, Software and Solver.
He interconnects Robust optimization, Optimization problem, Set and Pareto principle in the investigation of issues within Optimal control.
His research in Nonlinear system intersects with topics in Differential algebraic equation, Wind speed, Power and Direct multiple shooting method.
His most cited work include:
- ACADO toolkit—An open-source framework for automatic control and dynamic optimization (641 citations)
- qpOASES: a parametric active-set algorithm for quadratic programming (610 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?
Moritz Diehl spends much of his time researching Control theory, Model predictive control, Mathematical optimization, Optimal control and Nonlinear system.
His Control theory research focuses on Wind power and how it relates to Aerospace engineering, Airfoil, Energy and Wind speed.
His Model predictive control research is multidisciplinary, incorporating perspectives in Control engineering, Algorithm, Nonlinear model and Solver.
His Mathematical optimization study integrates concerns from other disciplines, such as Nonlinear programming and Convex optimization.
His Optimal control research is multidisciplinary, incorporating elements of Discretization, Power, Robust control and Direct multiple shooting method.
The various areas that Moritz Diehl examines in his Nonlinear system study include Estimation theory, Computation and Applied mathematics.
He most often published in these fields:
- Control theory (45.45%)
- Model predictive control (40.53%)
- Mathematical optimization (38.07%)
What were the highlights of his more recent work (between 2017-2021)?
- Model predictive control (40.53%)
- Control theory (45.45%)
- Mathematical optimization (38.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Model predictive control, Control theory, Mathematical optimization, Optimal control and Applied mathematics.
His Model predictive control study combines topics from a wide range of disciplines, such as Stability, Nonlinear model, Nonlinear system, Control theory and Extension.
His work in Nonlinear system addresses subjects such as Computation, which are connected to disciplines such as Software.
His work on Trajectory as part of general Control theory research is frequently linked to Central processing unit, Grey box model and Flux linkage, thereby connecting diverse disciplines of science.
Moritz Diehl combines subjects such as Homotopy, Collision avoidance and Motion planning with his study of Mathematical optimization.
His studies deal with areas such as Wind power, Actuator, Algorithm and Sequential quadratic programming as well as Optimal control.
Between 2017 and 2021, his most popular works were:
- CasADi: a software framework for nonlinear optimization and optimal control (543 citations)
- From linear to nonlinear MPC: bridging the gap via the real-time iteration (81 citations)
- In-Vehicle Realization of Nonlinear MPC for Gasoline Two-Stage Turbocharging Airpath Control (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Quantum mechanics
His scientific interests lie mostly in Model predictive control, Optimal control, Mathematical optimization, Nonlinear system and Algorithm.
His Model predictive control research includes elements of Control theory, Nonlinear model, Quadratic programming, Software and Contraction.
His work on Stability and Control theory as part of general Control theory study is frequently linked to Software framework, bridging the gap between disciplines.
Moritz Diehl has researched Optimal control in several fields, including Nonlinear programming, Battery, Integer, Differential equation and Stochastic model predictive control.
His Mathematical optimization study combines topics in areas such as Nonlinear dynamic systems, Motion planning and Motion.
His studies in Algorithm integrate themes in fields like Initialization and Trajectory.
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