Jolly Atit Shah

About

Jolly Atit Shah’s research focuses on the kinematics, dynamics, and control of robotic manipulators, with a particular emphasis on planar and articulated robot arms. His major contributions lie in applying computational methods—such as artificial neural networks and computed torque control—to solve forward and inverse kinematic problems for multi-degree-of-freedom (DOF) systems. Notably, his work on dynamic analysis and PID computed torque control for two-link robot manipulators (20 citations) provides a foundational framework for designing precise, high-speed robotic controllers used in manufacturing. Shah also advanced kinematic modeling of 3-DOF and 5-DOF robots, including the Pravak Robot Arm, using Denavit-Hartenberg parameters to accurately predict end-effector positions. His papers, cited over 60 times collectively, demonstrate lasting impact in robotics education and applied automation. By bridging classical robot theory with neural network-based solutions, Shah’s research offers practical tools for students and engineers working on robot arm design, trajectory planning, and real-time control systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Dynamic Analysis Of Two Link Robot Manipulator For Control Design Using PID Computed Torque Control.

   20 citations · 2016
2. 2
   Kinematic Analysis of 3-DOF Planer Robot Using Artificial Neural Network

   13 citations · 2012
3. 3
   End-Effector Position Analysis Using Forward Kinematics For 5 Dof Pravak Robot Arm

   11 citations · 2013
4. 4
   End-Effector Position Analysis Using Forward Kinematics For 5 Dof Pravak Robot Arm

   10 citations · 2013
5. 5
   Kinematic Analysis Of 2-Dof Planer Robot Using Artificial Neural Network

   4 citations · 2011

Key Collaborators

Contact & Links