Papers
38
Total Citations
426
H-Index
12
About
Farshad Khorrami is a prominent researcher whose work spans nonlinear control systems, robotics, autonomous systems, and machine learning-based safety frameworks. With a career stretching from foundational theoretical contributions in the early 1990s to cutting-edge work in autonomous vehicle security and safety-critical control, Khorrami has established himself as a versatile and influential figure in control engineering and robotics. His early landmark contributions include robust adaptive control of brushless DC motors and robotic manipulators, addressing parametric and dynamic uncertainties in electromechanical systems — work that has garnered nearly 60 citations. His experimental results on flexible manipulator control using endpoint acceleration feedback demonstrated a rare blend of theoretical rigor and practical implementation. More recently, Khorrami has pioneered differentiable optimization-based Control Barrier Functions (CBFs) for safe robot navigation, obstacle avoidance, and occlusion-free visual servoing, reflecting a sophisticated integration of optimization and safety-critical control theory. His research portfolio also encompasses unmanned aerial manipulation, decentralized robot control, adversarial attacks on autonomous vehicle perception systems, and anomaly monitoring for assured autonomy. This breadth — from Lyapunov-based stability analysis to deep learning architectures for sensor fusion — underscores Khorrami's ability to bridge classical control theory with modern AI-driven robotics, making his work highly relevant for researchers navigating today's autonomous systems landscape.
Research Focus
Key Achievements
Top Papers
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- 4Adaptive Control Approaches for an Unmanned Aerial Manipulation System24 citations · 2020
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