What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Artificial intelligence
- Control theory
Control theory, Feed forward, Nonlinear system, Vibration and Control engineering are his primary areas of study.
As part of his studies on Control theory, Santosh Devasia often connects relevant areas like Minimum phase.
Santosh Devasia focuses mostly in the field of Feed forward, narrowing it down to matters related to Robust control and, in some cases, Repetitive control and Dynamic positioning.
The study incorporates disciplines such as Acoustics, Piezoelectricity, Partial differential equation, Vibration control and Bounded function in addition to Nonlinear system.
His research integrates issues of Actuator and Search algorithm in his study of Vibration.
The various areas that Santosh Devasia examines in his Control engineering study include Iterative method and Electronic engineering.
His most cited work include:
- A Survey of Control Issues in Nanopositioning (882 citations)
- Nonlinear inversion-based output tracking (701 citations)
- Creep, Hysteresis, and Vibration Compensation for Piezoactuators: Atomic Force Microscopy Application (627 citations)
What are the main themes of his work throughout his whole career to date?
Santosh Devasia spends much of his time researching Control theory, Actuator, Feed forward, Control engineering and Vibration.
As part of the same scientific family, Santosh Devasia usually focuses on Control theory, concentrating on Tracking and intersecting with Iterative learning control.
His research in Actuator intersects with topics in Dual stage and Minification.
His Feed forward study which covers Electronic engineering that intersects with Piezoelectric actuators.
His Control engineering research includes themes of Robot and Control.
His Vibration research is multidisciplinary, incorporating elements of Microfluidics, Waveform, Bandwidth and Voltage.
He most often published in these fields:
- Control theory (49.38%)
- Actuator (19.09%)
- Feed forward (18.26%)
What were the highlights of his more recent work (between 2017-2021)?
- Robot (8.71%)
- Artificial intelligence (11.20%)
- Control theory (49.38%)
In recent papers he was focusing on the following fields of study:
His main research concerns Robot, Artificial intelligence, Control theory, Self reinforced and Distributed computing.
His study on Robot also encompasses disciplines like
- Iterative learning control that intertwine with fields like Feed forward, Gait and Power,
- Task and related Control engineering,
- Reduction, which have a strong connection to Deformation, Work and Human–computer interaction.
His Feed forward study incorporates themes from Kernel regression and Kernel.
Santosh Devasia has researched Control engineering in several fields, including Acoustics, Electromagnetic field, Electromagnet, Finite element method and Actuator.
His Artificial intelligence study also includes fields such as
- Computer vision, which have a strong connection to Activity recognition and Deep learning,
- Machine learning which is related to area like Frequency domain and Tracking error.
Santosh Devasia has included themes like Work and Variable in his Control theory study.
Between 2017 and 2021, his most popular works were:
- Iterative Machine Learning for Output Tracking (10 citations)
- Iterative machine learning for precision trajectory tracking with series elastic actuators (7 citations)
- Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks (7 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Artificial intelligence
- Control theory
Santosh Devasia mainly focuses on Artificial intelligence, Convergence, Machine learning, Iterative learning control and Robot.
His Machine learning research also works with subjects such as
- Data modeling, Iterative method, Bounded function, System dynamics and Content most often made with reference to Frequency domain,
- Tracking error together with Reduction, Noise, Operating point, Robotic arm and Output impedance.
His Iterative learning control research incorporates themes from Gait, Torque and Trajectory.
His study in Robot is interdisciplinary in nature, drawing from both Control engineering, Scheduling, Job shop scheduling and Task.
His studies deal with areas such as Automation and Teleoperation as well as Control engineering.
There are a combination of areas like Contact force and Feed forward integrated together with his Robot learning study.
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