Afshin Banazadeh
Papers
4
Total Citations
60
H-Index
4
About
Afshin Banazadeh is a leading researcher in autonomous aerial vehicle control and guidance systems, with a focus on fault-tolerant and adaptive control strategies. His most cited work introduces a novel fault-tolerant model predictive control (MPC) framework for trajectory tracking, leveraging a generalized online sequential extreme learning machine to identify actuator faults in real time—a contribution that has garnered 32 citations for its robustness in safety-critical flight operations. Banazadeh further advances the field with disturbance observer-based adaptive neural guidance and control using composite learning (12 citations), enabling aircraft to maintain stability under uncertain dynamics. His earlier work on near-optimal trajectory generation employs compound B-spline interpolation and minimum distance criteria with dynamical feasibility correction (9 citations), showcasing his ability to merge computational efficiency with real-world constraints. Additionally, his development and instrumentation of a Coanda air vehicle (7 citations) demonstrates hands-on expertise in dynamic identification and mathematical modeling for novel airframes. Banazadeh’s research directly impacts the reliability and autonomy of unmanned systems, making him a key figure in modern aerospace control engineering.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4