What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mechanical engineering
John T. Wen mostly deals with Control theory, Control engineering, Kinematics, Robot kinematics and Motion control.
His study brings together the fields of Robot control and Control theory.
The Control engineering study combines topics in areas such as Robot, Control, Robotic arm and Passivity.
The study incorporates disciplines such as Singularity, Beam and Compensation in addition to Kinematics.
John T. Wen combines subjects such as Motion planning and Linear-quadratic regulator with his study of Robot kinematics.
His Attitude control research includes elements of Quaternion and Rigid body.
His most cited work include:
- The attitude control problem (879 citations)
- Galerkin approximations of the generalized Hamilton-Jacobi-Bellman equation (464 citations)
- Attitude control without angular velocity measurement: a passivity approach (371 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Control theory, Control engineering, Robot, Nonlinear system and Control theory.
His studies examine the connections between Control theory and genetics, as well as such issues in Robot control, with regards to Robot end effector.
His research integrates issues of Kinematics, Motion control, Control, Robot kinematics and Robustness in his study of Control engineering.
John T. Wen has included themes like Singularity and Motion planning in his Kinematics study.
The various areas that John T. Wen examines in his Robot study include Simulation and Trajectory.
His Nonlinear system study combines topics from a wide range of disciplines, such as Iterative method, Mathematical optimization and Linear system.
He most often published in these fields:
- Control theory (57.66%)
- Control engineering (21.56%)
- Robot (17.14%)
What were the highlights of his more recent work (between 2013-2021)?
- Control theory (57.66%)
- Robot (17.14%)
- Control theory (11.69%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Control theory, Robot, Control theory, Feed forward and Circadian rhythm.
His Control theory study combines topics in areas such as Control engineering, Model predictive control and Heat flux.
His Control engineering research is multidisciplinary, incorporating perspectives in Control and Robot control, Mobile robot.
His Robot research includes themes of Setpoint, Robotic arm and Trajectory.
His Control theory study incorporates themes from Temperature control, Simulation and Humidity.
His work deals with themes such as Passivity and Evaporator, which intersect with Feed forward.
Between 2013 and 2021, his most popular works were:
- Modeling and control of color tunable lighting systems (45 citations)
- A Sensor-Based Dual-Arm Tele-Robotic System (44 citations)
- BEES: Real-time occupant feedback and environmental learning framework for collaborative thermal management in multi-zone, multi-occupant buildings (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Mechanical engineering
John T. Wen spends much of his time researching Control theory, Simulation, Control theory, Temperature control and Convergence.
His Control theory research integrates issues from Electronics cooling, Heat transfer and Heat flux.
The concepts of his Simulation study are interwoven with issues in Testbed, Assistive robot, Robot control and Robotic arm.
His studies deal with areas such as Robot, Robot end effector, Mobile robot and Mass flow as well as Control theory.
His Temperature control research also works with subjects such as
- Building automation that connect with fields like Stability, Minification and Singular perturbation,
- Air conditioning together with Iterative learning control.
He interconnects Algorithm, Algorithm design and Consensus in the investigation of issues within Convergence.
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.
