What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Quantum mechanics
- Control theory
Jean-Jacques E. Slotine mainly focuses on Control theory, Nonlinear system, Control engineering, Adaptive control and Control theory.
His study in Control theory is interdisciplinary in nature, drawing from both Teleoperation and Synchronization.
His work carried out in the field of Nonlinear system brings together such families of science as State vector, Multivariate statistics and Contraction.
His Control engineering research includes themes of Robot kinematics, Robot, Robotics and Range.
His biological study spans a wide range of topics, including Fuzzy sliding mode control, Feedback linearization and Manufacturing engineering.
His Adaptive control research is multidisciplinary, incorporating perspectives in Sylvester's law of inertia, Robot control, Adaptive algorithm and Motion control.
His most cited work include:
- Applied Nonlinear Control (11960 citations)
- Controllability of complex networks (2180 citations)
- Gaussian networks for direct adaptive control (1885 citations)
What are the main themes of his work throughout his whole career to date?
Jean-Jacques E. Slotine spends much of his time researching Control theory, Nonlinear system, Adaptive control, Control engineering and Robot.
His Control theory study frequently links to other fields, such as Robot control.
His Nonlinear system research includes elements of Stability, Convergence, Observer, Applied mathematics and Contraction.
His Adaptive control research is multidisciplinary, relying on both Artificial neural network, Algorithm, Robot kinematics and Robustness.
His research on Control engineering frequently connects to adjacent areas such as Motion control.
His study with Robot involves better knowledge in Artificial intelligence.
He most often published in these fields:
- Control theory (46.44%)
- Nonlinear system (34.15%)
- Adaptive control (20.64%)
What were the highlights of his more recent work (between 2017-2021)?
- Nonlinear system (34.15%)
- Control theory (46.44%)
- Contraction (14.99%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Nonlinear system, Control theory, Contraction, Adaptive control and Dynamical systems theory.
His Nonlinear system study incorporates themes from Bounded function, Mathematical optimization, Model predictive control and System dynamics.
The Control theory study combines topics in areas such as Aerodynamics and Artificial intelligence.
Jean-Jacques E. Slotine has researched Contraction in several fields, including Nonlinear control, Robotics, Metric, Regular polygon and Applied mathematics.
The various areas that Jean-Jacques E. Slotine examines in his Nonlinear control study include Feedback linearization, Backstepping, Lyapunov stability and Convex optimization.
Jean-Jacques E. Slotine combines subjects such as Artificial neural network, Recurrent neural network, State space, Kalman filter and Algorithm with his study of Dynamical systems theory.
Between 2017 and 2021, his most popular works were:
- Linear Matrix Inequalities for Physically Consistent Inertial Parameter Identification: A Statistical Perspective on the Mass Distribution (41 citations)
- Avoidance of Convex and Concave Obstacles With Convergence Ensured Through Contraction (24 citations)
- Dynamic Tube MPC for Nonlinear Systems (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Quantum mechanics
- Control theory
His primary areas of study are Control theory, Nonlinear system, Adaptive control, Contraction and Regularization.
The concepts of his Control theory study are interwoven with issues in Robot, Semidefinite programming and Convex optimization.
Jean-Jacques E. Slotine studies Nonlinear dynamical systems which is a part of Nonlinear system.
His work focuses on many connections between Adaptive control and other disciplines, such as Nonlinear control, that overlap with his field of interest in Backstepping, Feedback linearization and Robustness.
His research integrates issues of Random matrix, Robotics and Curse of dimensionality in his study of Regularization.
The study incorporates disciplines such as Hilbert space and Conjecture in addition to Artificial intelligence.
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