What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Electrical engineering
His primary areas of study are Control theory, Optimal control, Control theory, Artificial neural network and Nonlinear system.
His Control theory research is multidisciplinary, incorporating elements of Control engineering and Reinforcement learning.
His Optimal control research includes themes of Algebraic Riccati equation, Dynamic programming and Bellman equation.
He works mostly in the field of Control theory, limiting it down to topics relating to Trajectory and, in certain cases, Mobile robot, as a part of the same area of interest.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Passivity, Stability, Dynamical system, Bounded function and Robot control.
His study looks at the intersection of Nonlinear system and topics like Inner loop with Singular perturbation.
His most cited work include:
- Optimal Control (2919 citations)
- Aircraft Control and Simulation (2101 citations)
- A survey of linear singular systems (1165 citations)
What are the main themes of his work throughout his whole career to date?
Frank L. Lewis mostly deals with Control theory, Control theory, Control engineering, Artificial neural network and Nonlinear system.
His study in Control theory concentrates on Adaptive control, Optimal control, Control system, Stability and Lyapunov function.
As a member of one scientific family, Frank L. Lewis mostly works in the field of Optimal control, focusing on Reinforcement learning and, on occasion, System dynamics.
Within one scientific family, he focuses on topics pertaining to Trajectory under Control theory, and may sometimes address concerns connected to Tracking.
His Control engineering study combines topics from a wide range of disciplines, such as Control, Robustness and Fuzzy logic.
His research integrates issues of Motion control, Discrete time and continuous time, Bounded function and Feed forward in his study of Artificial neural network.
He most often published in these fields:
- Control theory (57.29%)
- Control theory (25.93%)
- Control engineering (22.88%)
What were the highlights of his more recent work (between 2017-2021)?
- Control theory (57.29%)
- Control theory (25.93%)
- Reinforcement learning (14.41%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Control theory, Control theory, Reinforcement learning, Optimal control and Nonlinear system.
His studies deal with areas such as Artificial neural network, Control, Tracking and Multi-agent system as well as Control theory.
His Control theory study incorporates themes from Control system, Actuator and Position.
His Reinforcement learning research includes themes of Observer, Mathematical optimization, Hamilton–Jacobi–Bellman equation and System dynamics.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Convergence and Linear system.
The concepts of his Optimal control study are interwoven with issues in Q-learning, Discrete time and continuous time, Algorithm, Dynamic programming and Process control.
Between 2017 and 2021, his most popular works were:
- Optimal and Autonomous Control Using Reinforcement Learning: A Survey (210 citations)
- Robust Formation Control for Multiple Quadrotors With Nonlinearities and Disturbances (71 citations)
- Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Convergence Analysis (60 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Artificial intelligence
- Electrical engineering
Frank L. Lewis spends much of his time researching Control theory, Control theory, Multi-agent system, Optimal control and Reinforcement learning.
He has researched Control theory in several fields, including Artificial neural network, Control and Tracking.
Frank L. Lewis combines subjects such as Control system, Aerodynamics, Attitude control, Vehicle dynamics and Position with his study of Control theory.
His studies in Optimal control integrate themes in fields like Discrete time and continuous time, Dynamic programming, Approximation algorithm, Feed forward and Process control.
His work carried out in the field of Reinforcement learning brings together such families of science as Control engineering and Mathematical optimization.
In general Nonlinear system study, his work on Adaptive control and Robust control often relates to the realm of Regulator, thereby connecting several areas of interest.
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