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About

Heydar Toossian Shandiz is a robotics researcher whose work centers on advanced control, identification, and path planning for robotic systems. His major contributions lie at the intersection of fractional-order modeling and adaptive control, particularly for flexible-joint manipulators. In his most cited work, he developed an adaptive tracking controller that leverages time scale separation and parameter separation techniques to handle parametric uncertainties in single-link flexible-joint robots. This approach effectively couples positive functions of linearly connected parameters, offering robust performance despite system nonlinearities. His second key contribution involves fractional subspace identification, where he introduced a continuous-time fractional operator to estimate state-space models of MIMO systems from stochastic data—a novel method for handling fractional derivatives in the time domain. Shandiz has also explored simultaneous fault detection and control (SFDC) for robots using linear fractional-order models, formulating new LMI-based rules for Luenberger-type observers. Additionally, his work on masking-based path planning addresses redundancy and singularity issues. With over 20 citations across his most prominent papers, Shandiz’s research is foundational for engineers seeking to improve the precision, fault tolerance, and autonomy of robotic manipulators in uncertain environments.