What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Mechanical engineering
Chun-Yi Su spends much of his time researching Control theory, Adaptive control, Nonlinear system, Control theory and Control engineering.
His studies in Control theory integrate themes in fields like Fuzzy logic and Motion control.
His Adaptive control research is multidisciplinary, relying on both Control system, Linear system, Dynamical system, Exponential stability and Lyapunov function.
His work on Nonlinear control as part of his general Nonlinear system study is frequently connected to Hysteresis, thereby bridging the divide between different branches of science.
His Control theory study combines topics in areas such as Discrete time and continuous time and System identification.
His Control engineering study incorporates themes from Testbed, Robot control and Nonholonomic system.
His most cited work include:
- Robust adaptive control of a class of nonlinear systems with unknown backlash-like hysteresis (468 citations)
- Robust adaptive control of a class of nonlinear systems with unknown dead-zone (439 citations)
- Adaptive control of a class of nonlinear systems with fuzzy logic (342 citations)
What are the main themes of his work throughout his whole career to date?
Chun-Yi Su mainly focuses on Control theory, Adaptive control, Nonlinear system, Control theory and Control engineering.
His research ties Robot and Control theory together.
His biological study spans a wide range of topics, including Stability, Exponential stability, Motion control, Tracking error and Trajectory.
His work on Nonlinear control as part of general Nonlinear system research is frequently linked to Hysteresis, bridging the gap between disciplines.
His Control theory research incorporates elements of Observer, Fuzzy logic and Nonholonomic system.
His Control engineering study combines topics from a wide range of disciplines, such as Control and Robustness.
He most often published in these fields:
- Control theory (87.44%)
- Adaptive control (39.07%)
- Nonlinear system (35.35%)
What were the highlights of his more recent work (between 2015-2021)?
- Control theory (87.44%)
- Robot (21.40%)
- Control theory (30.47%)
In recent papers he was focusing on the following fields of study:
Control theory, Robot, Control theory, Nonlinear system and Artificial neural network are his primary areas of study.
His research related to Actuator, Adaptive control, Lyapunov function, Trajectory and Control system might be considered part of Control theory.
Chun-Yi Su interconnects Simulation and Exoskeleton in the investigation of issues within Robot.
His research in Control theory intersects with topics in Stability, Fuzzy logic and Mobile robot.
His work carried out in the field of Nonlinear system brings together such families of science as Function, Particle swarm optimization and Bounded function.
His Artificial neural network research is multidisciplinary, incorporating elements of Observer, Robustness and Robot manipulator.
Between 2015 and 2021, his most popular works were:
- Modeling and Control of Piezo-Actuated Nanopositioning Stages: A Survey (277 citations)
- Neural Control of Bimanual Robots With Guaranteed Global Stability and Motion Precision (220 citations)
- Teleoperation Control Based on Combination of Wave Variable and Neural Networks (207 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Mechanical engineering
His primary areas of study are Control theory, Robot, Artificial neural network, Nonlinear system and Control theory.
The various areas that Chun-Yi Su examines in his Control theory study include Control engineering and Robot kinematics.
His Robot research integrates issues from Simulation, Exoskeleton and Trajectory.
His research integrates issues of Algorithm design and Particle swarm optimization in his study of Nonlinear system.
His studies deal with areas such as Stability, Object, Stiffness and Fuzzy logic as well as Control theory.
His Adaptive control research focuses on Tracking error and how it relates to Nonlinear control.
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