What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Machine learning
His primary areas of study are Iterative learning control, Control theory, Mathematical optimization, Artificial intelligence and Control engineering.
His work deals with themes such as Stability, Discrete time and continuous time, Iterative method, Markov chain and Intelligent control, which intersect with Iterative learning control.
His Control theory research incorporates themes from Tracking and Monotonic function.
Kevin L. Moore has researched Mathematical optimization in several fields, including Uniform consensus and Consensus.
In most of his Artificial intelligence studies, his work intersects topics such as Machine learning.
Kevin L. Moore combines subjects such as Robot, Motion planning and Autonomous robot with his study of Control engineering.
His most cited work include:
- Iterative Learning Control: Brief Survey and Categorization (1088 citations)
- Iterative Learning Control for Deterministic Systems (583 citations)
- Discretization schemes for fractional-order differentiators and integrators (488 citations)
What are the main themes of his work throughout his whole career to date?
Control theory, Iterative learning control, Artificial intelligence, Control engineering and Iterative method are his primary areas of study.
His work is connected to Control system, Control theory, Adaptive control, Robust control and Stability, as a part of Control theory.
His Iterative learning control study also includes fields such as
- Mathematical optimization, which have a strong connection to Robustness,
- Interval most often made with reference to Markov chain.
His Artificial intelligence research includes themes of Machine learning and Computer vision.
His biological study spans a wide range of topics, including Control, Trajectory, Automatic control and Nonlinear system.
His Iterative method research is multidisciplinary, incorporating elements of Feed forward and Internal model.
He most often published in these fields:
- Control theory (41.63%)
- Iterative learning control (33.48%)
- Artificial intelligence (20.17%)
What were the highlights of his more recent work (between 2009-2020)?
- Control theory (41.63%)
- Iterative learning control (33.48%)
- Network topology (4.29%)
In recent papers he was focusing on the following fields of study:
His main research concerns Control theory, Iterative learning control, Network topology, Iterative method and Mathematical optimization.
His Control theory research incorporates elements of Control engineering and Lipschitz continuity.
His work carried out in the field of Iterative learning control brings together such families of science as Convergence, Robust control, Tracking error, Adaptive control and Trajectory.
As a part of the same scientific study, Kevin L. Moore usually deals with the Adaptive control, concentrating on Domain and frequently concerns with Repetitive control, Multivariable calculus and Monotonic function.
His Iterative method research includes elements of Optimal control and Internal model.
The concepts of his Mathematical optimization study are interwoven with issues in Distributed algorithm, Salient, Tuple and Rational function.
Between 2009 and 2020, his most popular works were:
- Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems (172 citations)
- Trajectory‐keeping in satellite formation flying via robust periodic learning control (91 citations)
- Robust Iterative Learning Control for Nonrepetitive Uncertain Systems (85 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Machine learning
Kevin L. Moore spends much of his time researching Control theory, Iterative learning control, Mathematical optimization, Control system and Stability.
His Control theory research integrates issues from Monotonic function, Lipschitz continuity and Computational problem.
His Iterative learning control study combines topics in areas such as Convergence, Tracking, Tracking error, Bounded function and Trajectory.
His research integrates issues of Domain, Iterative method, Adaptive control and Repetitive control in his study of Convergence.
The Mathematical optimization study combines topics in areas such as Theoretical computer science, Node and Rational function.
The various areas that Kevin L. Moore examines in his Control system study include Contrast, Software deployment, Industrial engineering and Implementation.
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