Papers
33
Total Citations
562
H-Index
12
About
Vahid Azimirad’s research lies at the intersection of advanced robotics control, bio-inspired intelligence, and autonomous manipulation. His major contributions span three core areas: robust nonlinear control of robotic manipulators, optimal path planning for mobile manipulators, and neuromorphic learning systems. His most influential work, a 2016 paper on fractional-order adaptive backstepping control (134 citations), pioneered methods to stabilize n-DOF robotic arms under model uncertainties and external disturbances—a critical challenge in real-world automation. He further advanced this with adaptive backstepping based on state augmentation (76 citations). In mobile robotics, Azimirad developed hierarchical optimal control strategies to determine maximum load-carrying capacity while ensuring tip-over stability in obstacle-cluttered environments (60 citations). More recently, he has broken new ground in brain-inspired robotics, introducing a consecutive hybrid spiking-convolutional neural controller for sequential decision-making (28 citations) and experimentally validating reinforcement learning through thalamo-cortico-thalamic spiking architectures (14 citations). His work also extends to brain-robot interfaces, using SVM to classify EEG signals for motor imagery (14 citations). With over 400 total citations, Azimirad’s research uniquely bridges classical control theory and emerging neuromorphic computing, offering students a compelling model of how rigorous mathematical foundations can drive next-generation autonomous systems.
Research Focus
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