Mahdi Aliyari Shoorehdeli

K.N.Toosi University of Technology

Papers

16

Total Citations

232

H-Index

9

About

Mahdi Aliyari Shoorehdeli is a leading researcher in intelligent robotics and nonlinear control systems, with a focus on adaptive neural networks, fuzzy control, and motion planning for autonomous mobile robots. His most impactful work includes developing adaptive recurrent neural networks with Lyapunov stability learning rules for robot dynamic identification (44 citations), and pioneering tracking control methods for nonholonomic mobile robots using ANFIS (42 citations). He has made significant contributions to type-2 fuzzy control for flexible-joint robots (40 citations) and actuator fault tolerance in nonlinear model predictive control systems (18 citations). His innovative Time-variant Artificial Potential Field (TAPF) method (12 citations) represents a breakthrough in power-optimized motion planning for autonomous space robots, addressing critical energy efficiency challenges. Shoorehdeli has also advanced biped locomotion through hybrid CPG-ZMP controllers and explored biomechatronic applications for emotional support robots. His work consistently integrates theoretical stability guarantees with practical robotic implementations, earning him recognition for bridging control theory and real-world robotics. With over 200 citations across his top publications, Shoorehdeli continues to influence intelligent control systems and autonomous navigation.

Research Focus

Key Achievements

9
H-Index
16
Papers
232
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
Adaptive recurrent neural network with Lyapunov stability learning rules for robot dynamic terms identification
44 citations · 2017
📈 Most Prolific Year: 2017 (3 Papers)
🤝 Key Collaborators: 23
🏛 Institutions: K.N.Toosi University of Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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