What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Electrical engineering
Kok Kiong Tan focuses on Control theory, Control engineering, Nonlinear system, Adaptive control and Control theory.
His studies in Control theory integrate themes in fields like Artificial neural network and Motion control.
The various areas that Kok Kiong Tan examines in his Control engineering study include Relay, Automatic control, Actuator, Noise and Injector.
His Nonlinear system research incorporates elements of Stability, Model predictive control and Observer.
His Adaptive control study which covers Compensation that intersects with Trajectory.
His Control theory study incorporates themes from Iterative learning control, Exponential stability, Decentralised system and Transfer function.
His most cited work include:
- Robust adaptive numerical compensation for friction and force ripple in permanent-magnet linear motors (186 citations)
- Decentralized control design for large-scale systems with strong interconnections using neural networks (114 citations)
- Precision motion control with disturbance observer for pulsewidth-modulated-driven permanent-magnet linear motors (111 citations)
What are the main themes of his work throughout his whole career to date?
Control theory, Control engineering, Control theory, Motion control and PID controller are his primary areas of study.
His research investigates the connection between Control theory and topics such as Relay that intersect with issues in Limit cycle.
His Control engineering study combines topics in areas such as Control, Model predictive control, Actuator and Robustness.
He regularly ties together related areas like Process control in his Control theory studies.
His work focuses on many connections between Motion control and other disciplines, such as Linear motor, that overlap with his field of interest in Ripple.
As a member of one scientific family, Kok Kiong Tan mostly works in the field of Nonlinear system, focusing on Artificial neural network and, on occasion, Stability and Algorithm.
He most often published in these fields:
- Control theory (55.99%)
- Control engineering (29.81%)
- Control theory (17.83%)
What were the highlights of his more recent work (between 2015-2021)?
- Control theory (55.99%)
- Control engineering (29.81%)
- Motion control (14.76%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Control theory, Control engineering, Motion control, Control theory and Feed forward.
Control theory and Match moving are frequently intertwined in his study.
His Control engineering study combines topics from a wide range of disciplines, such as Automation, Control, Model predictive control and System dynamics.
His work carried out in the field of Motion control brings together such families of science as Artificial neural network, Motion system, Work, Nonlinear system and Kalman filter.
His Control theory study focuses on PID controller in particular.
His research on Feed forward also deals with topics like
- Feedforward neural network, which have a strong connection to Control system,
- Compensation which is related to area like Inverse hysteresis.
Between 2015 and 2021, his most popular works were:
- Development of a Genetic-Algorithm-Based Nonlinear Model Predictive Control Scheme on Velocity and Steering of Autonomous Vehicles (60 citations)
- An RBF neural network approach towards precision motion system with selective sensor fusion (40 citations)
- Vision-based approach towards lane line detection and vehicle localization (37 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Electrical engineering
His scientific interests lie mostly in Control theory, Control theory, Motion control, Control engineering and Motion system.
His work on PID controller and Robust control as part of his general Control theory study is frequently connected to Cartesian coordinate system, thereby bridging the divide between different branches of science.
State space, Stability and Optimal control is closely connected to Decentralised system in his research, which is encompassed under the umbrella topic of Control theory.
His research in Motion control intersects with topics in Work in process and Compensation.
His Control engineering research is multidisciplinary, incorporating elements of Discretization, System dynamics and Nonlinear system.
The Motion system study combines topics in areas such as Artificial neural network, Stability and Match moving.
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