What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mechanical engineering
His main research concerns Control theory, Control engineering, Control theory, Control system and Adaptive control.
As part of his studies on Control theory, Masayoshi Tomizuka often connects relevant subjects like Motion control.
The various areas that Masayoshi Tomizuka examines in his Control engineering study include Digital control, Robot, Trajectory and Automatic control.
His Control theory course of study focuses on Stability and Convergence.
His Control system research is multidisciplinary, incorporating elements of Servomotor, Open-loop controller, Machine tool, Servomechanism and PID controller.
His work on Backstepping as part of general Adaptive control study is frequently linked to Parametric statistics, therefore connecting diverse disciplines of science.
His most cited work include:
- Zero Phase Error Tracking Algorithm for Digital Control (1270 citations)
- Fuzzy gain scheduling of PID controllers (623 citations)
- Automated vehicle control developments in the PATH program (608 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Control theory, Control engineering, Control theory, Control system and Artificial intelligence.
His research ties Motion control and Control theory together.
His research integrates issues of Trajectory, Control, Actuator, Robot and Robustness in his study of Control engineering.
As part of one scientific family, Masayoshi Tomizuka deals mainly with the area of Actuator, narrowing it down to issues related to the Torque, and often Simulation.
His Control theory research is multidisciplinary, relying on both Stability, Tracking and Digital control.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision.
He most often published in these fields:
- Control theory (59.24%)
- Control engineering (31.48%)
- Control theory (26.97%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (14.77%)
- Machine learning (5.66%)
- Control theory (59.24%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Control theory, Robot and Trajectory.
His work in Artificial intelligence tackles topics such as Computer vision which are related to areas like GRASP and Robustness.
Control theory is closely attributed to Tracking in his work.
His Robot study combines topics in areas such as Control engineering, Task and Task.
His work deals with themes such as Work, Vehicle dynamics and Automation, which intersect with Trajectory.
He interconnects Stability, Control system and Motion control in the investigation of issues within Control theory.
Between 2017 and 2021, his most popular works were:
- INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps. (66 citations)
- RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement (61 citations)
- Probabilistic Prediction of Vehicle Semantic Intention and Motion (52 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Mechanical engineering
His primary areas of study are Artificial intelligence, Machine learning, Trajectory, Probabilistic logic and Artificial neural network.
His Machine learning study combines topics in areas such as Roundabout, Motion and Representation.
His Trajectory study also includes
- Vehicle dynamics which intersects with area such as Obstacle avoidance,
- Robot and related Component.
His Probabilistic logic study also includes fields such as
- Motion planning that connect with fields like Intelligent decision support system, Mathematical optimization and Optimal control,
- Data mining which connect with Duration.
His research investigates the connection with Artificial neural network and areas like Control theory which intersect with concerns in Computational complexity theory.
His Nonlinear system study is concerned with the larger field of Control theory.
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