Mohammad Taghavi

Papers

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About

Mohammad Taghavi is a researcher specializing in nonlinear control systems, fuzzy logic, and robotics, with a particular focus on flexible and continuum robot manipulators. His work addresses the critical challenge of controlling highly nonlinear, uncertain dynamical systems—a problem central to advanced robotics and automation. Taghavi’s most cited paper, “Nonlinear Fuzzy Model-base Technique to Compensate Highly Nonlinear Continuum Robot Manipulator” (2013, 28 citations), introduces a gradient descent optimization methodology for a position fuzzy-model-based computed torque controller (GDFCTC). This approach overcomes the limitations of pure computed torque control, which struggles with system uncertainties, by leveraging fuzzy logic to enhance robustness and precision. In his subsequent work, “Research on Minimum Intelligent Unit for Flexible Robot” (2015, 7 citations), Taghavi extends these ideas to design a high-performance PID-like fuzzy controller for multi-input, multi-output (MIMO) flexible robot manipulators, demonstrating effectiveness under uncertain conditions. With a cumulative impact of over 35 citations, Taghavi’s contributions are foundational for developing more adaptive, intelligent control strategies in soft and flexible robotics—a rapidly growing field with applications in medical devices, industrial automation, and human-robot interaction.