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About

Fazel Mohammadi is a rising researcher in intelligent control systems, with a focus on nonlinear dynamics, robotics, and multi-agent systems. His work centers on developing advanced computational intelligence methods—particularly Type-II fuzzy logic systems and neural networks—to solve complex control problems. In his highly cited 2019 paper, he introduced a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) using Interval Gaussian Type-II fuzzy sets for flexible-joint robot arm control, achieving 11 citations. That same year, he proposed a Type-II Fuzzy-PID controller for 3-PRS parallel robots, demonstrating high precision in industrial applications (10 citations). Mohammadi also designed a new intelligent control approach for nonlinear systems using Radial Basis Function Neural Networks (RBFNN) to approximate uncertain dynamics (7 citations). More recently, he tackled the challenging problem of state estimation in non-affine nonlinear multi-agent systems, developing a distributed neural network observer for unknown dynamics (2020). His contributions bridge theoretical advances in fuzzy logic and neural control with practical robotic applications, making him a notable voice in the field of intelligent automation and nonlinear system control.