Vahid Tavakol Aghaei
Istinye University, Bahçeşehir University, Sabancı Üniversitesi, AVL (Turkey)
Papers
8
Total Citations
82
H-Index
5
About
Vahid Tavakol Aghaei is a researcher at the forefront of intelligent control systems, specializing in the intersection of reinforcement learning, Bayesian methods, and bio-inspired optimization for nonlinear and underactuated robotic systems. His major contributions include pioneering the application of the Sand Cat Swarm Optimization (SCSO) algorithm for state feedback controller design in open-loop unstable systems—a novel approach that has garnered 26 citations since 2023. He has also advanced deep reinforcement learning for complex parallel robots, such as the Stewart platform, with parametric simulation in ROS and Gazebo (17 citations), addressing the challenges of closed-loop kinematics. Aghaei’s work on Bayesian trajectory control, including the use of Markov chain Monte Carlo methods for policy search in robotic manipulators (13 and 10 citations), has provided robust alternatives to traditional policy gradient algorithms, overcoming issues of slow convergence and local optima. His comparative study on classical and intelligent methods for stabilizing a dual-axis reaction wheel pendulum (6 citations) further demonstrates his expertise in tackling challenging control problems. With over 80 total citations, Aghaei’s research is shaping the future of adaptive, learning-based control for dynamic systems.
Research Focus
Key Achievements
Top Papers
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- 4A Markov chain Monte Carlo algorithm for Bayesian policy search10 citations · 2018
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