Alireza Khayatian
Papers
12
Total Citations
133
H-Index
7
About
Alireza Khayatian is a robotics and control systems researcher whose work centers on parallel robot kinematics, adaptive control theory, and intelligent computational methods for robotic systems. He is perhaps best known for his pioneering application of neural networks to solve the notoriously intractable forward kinematics problem of the HEXA parallel robot — work that garnered over 53 citations and demonstrated that data-driven approaches could effectively replace analytically unsolvable closed-form solutions. His 2008 contributions further extended this line of inquiry through wavelet-based neural architectures and Lagrangian dynamics formulations for inverse dynamics modeling of parallel robots, addressing the significant complexity and nonlinearity inherent in such systems. Khayatian has also made meaningful contributions to vision-based kinematic calibration, cooperative robot manipulator control, and adaptive backstepping strategies for electrically driven robots under kinematic and dynamic uncertainty. His more recent work on L₁ adaptive high-gain observers for Euler–Lagrange systems reflects a continuing commitment to robust, practically deployable control frameworks. Across more than a decade of research, Khayatian's cumulative impact spans both theoretical rigor and experimental validation, making his body of work a valuable resource for students and researchers working at the intersection of robotic kinematics, dynamics, and intelligent control design.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4Vision-based calibration of a Hexa parallel robot11 citations · 2014
- 5
- 6
- 7
- 8
- 9
- 10