Farhad Rajaee
Papers
1
Total Citations
52
H-Index
1
About
Farhad Rajaee is a control systems researcher whose work bridges intelligent neural architectures and robust nonlinear control for complex robotic systems. His primary research areas include sliding mode control, recurrent neural networks, and the dynamics of non-holonomic spherical robots. Rajaee’s most cited work, published in 2020, introduces a novel robust nonsingular sliding mode control scheme for a pendulum-driven spherical robot, integrating a recurrent neural network to handle input saturation and system uncertainties. This contribution has garnered 52 citations, reflecting its significance in addressing the challenging stabilization and trajectory tracking of underactuated, non-holonomic systems. By combining neural network adaptability with rigorous sliding mode theory, Rajaee’s approach offers a practical solution for real-world robotics where model inaccuracies and actuator limits are unavoidable. His research is particularly impactful for students and engineers working on autonomous mobile robots, offering a clear pathway from theoretical control design to implementation on hardware. Rajaee’s work stands as a valuable reference for those seeking to understand how intelligent control can overcome the inherent nonlinearities and constraints of spherical robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1