Ahmad Forouzan Tabar

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About

Ahmad Forouzan Tabar is a robotics researcher whose work centers on the control and modeling of bipedal locomotion systems. His primary research areas include neural network control, biped robot modeling, and pneumatic artificial muscle actuation. Tabar's major contributions lie in developing stable control architectures for novel biped robot designs, particularly those actuated by plated pneumatic artificial muscles—a technology offering high power-to-weight ratios and inherent compliance. His most cited work, "Controlling a New Biped Robot Model Since Walking Using Neural Network" (2007, 5 citations), introduced a neural network controller for a 6-degree-of-freedom biped model with slink structure. A complementary study, "Neural Network Control of a New Biped Robot Model with Back Propagation Algorithm" (2007, 3 citations), provided comparative analysis between neural network and PD control strategies for the same platform. Though his citation counts are modest, Tabar's early exploration of compliant actuation and intelligent control for biped robots represents foundational work in adaptive locomotion systems. His research bridges the gap between theoretical control methods and practical robotic applications, offering insights into how biologically-inspired actuators can be effectively regulated for dynamic walking tasks.