Taiki Ishida
Papers
2
Total Citations
42
H-Index
2
About
Taiki Ishida’s research centers on the intelligent control and dynamic modeling of industrial robot manipulators, with a particular focus on neural network-based approaches. His major contributions lie in developing novel methods that integrate neural networks with traditional control techniques to address the fundamental challenges of robot dynamics—specifically, parameter identification and trajectory tracking. In his most cited work (2006, 31 citations), Ishida introduced a two-step identification framework that uses neural networks to compensate for uncertain dynamics, significantly improving the accuracy of parameter estimation for robot manipulators. Building on this, his 2007 paper (11 citations) proposed a hybrid control scheme combining neural network technology with conventional methods to achieve precise trajectory tracking. These contributions are notable for bridging the gap between theoretical neural network applications and practical industrial robotics, offering robust solutions for real-world automation tasks. Ishida’s work has been influential in advancing adaptive control strategies, providing a foundation for researchers and engineers seeking to enhance the performance and reliability of robotic systems in manufacturing and beyond.
Research Focus
Key Achievements
Top Papers
- 1Neural Network Aided Dynamic Parameter Identification of Robot Manipulators31 citations · 2006
- 2