Aquib Mustafa

About

Aquib Mustafa is a control systems and robotics researcher whose work focuses on advanced trajectory tracking and intelligent manipulation for robotic systems. His most impactful contribution is an event-triggered sliding mode control approach for nonlinear systems, which addresses the trajectory tracking problem under disturbance—a paper that has garnered 80 citations for its innovative reduction of control updates while maintaining robust performance. Mustafa has also developed adaptive backstepping sliding mode control, enhanced by a nonlinear disturbance observer, to eliminate the effects of lumped uncertainties in robotic manipulators. Beyond control theory, he has explored machine learning solutions for robotics, using support vector regression and Kohonen self-organizing maps to solve complex inverse kinematics problems, and has integrated stereo-vision systems for object grasping. His work bridges theoretical control design with practical robotic applications, offering efficient, learning-based alternatives to traditional analytical methods. With a publication record spanning from 2016 to 2019, Mustafa’s research continues to influence the fields of nonlinear control, disturbance rejection, and autonomous manipulation.

Research Focus

Key Achievements

Top Papers

1. 1
   Event-triggered sliding mode control for trajectory tracking of nonlinear systems

   80 citations · 2019
2. 2
   Adaptive backstepping sliding mode control based on nonlinear disturbance observer for trajectory tracking of robotic manipulator

   14 citations · 2017
3. 3
   Inverse kinematics evaluation for robotic manipulator using support vector regression and kohonen self organizing map

   5 citations · 2016
4. 4
   Stereo-vision based object grasping using robotic manipulator

   4 citations · 2016

Key Collaborators

Contact & Links