Tarek Bensidhoum
Papers
1
Total Citations
26
H-Index
1
About
Dr. Tarek Bensidhoum is a leading researcher in advanced robotics and nonlinear control systems, with a focus on adaptive and iterative learning control strategies. His most-cited work, an adaptive P‑type iterative learning radial basis function (RBF) control scheme for robot manipulators, addresses critical challenges in trajectory tracking under unknown, iteration‑varying disturbances and input dead‑zone nonlinearities. This 2020 paper has garnered 26 citations, reflecting its impact on the field. Dr. Bensidhoum’s contributions lie in developing robust, real‑time control methods that enhance the precision and reliability of robotic systems operating in uncertain environments. By integrating RBF neural networks with iterative learning, he has provided a framework that compensates for complex, time‑varying uncertainties without requiring explicit system models. His work is particularly valuable for applications in industrial automation, rehabilitation robotics, and autonomous systems where high‑performance tracking is essential. Dr. Bensidhoum continues to advance the frontiers of intelligent control, making his research a key reference for engineers and scholars working on adaptive robotics and nonlinear system compensation.
Research Focus
Key Achievements
Top Papers
- 1