Papers

3

Total Citations

23

H-Index

2

About

Shahram Etemadi Haghighi is a researcher whose work lies at the intersection of robotics, swarm intelligence, and advanced control systems. His primary research areas include multi-robot coordination, bio-inspired swarm aggregation, and the application of intelligent optimization algorithms to complex mechanical systems. A key contribution is his development of an online velocity optimization method for robotic swarms using Particle Swarm Optimization (PSO), which maximizes coordination velocity in homogeneous multi-agent systems—a foundational step for efficient swarm flocking. He also explored swarm aggregation control through an emotional learning-based intelligent controller, offering a novel approach to decentralized motion in n-dimensional spaces. More recently, Haghighi has advanced the field of bipedal robotics by applying a multi-objective improved team game algorithm to achieve Pareto-optimal design of a fuzzy adaptive sliding mode controller for a three-link biped robot model. His most-cited work, with 11 citations, demonstrates the growing impact of his contributions to robust, adaptive control in humanoid locomotion. Through these efforts, Haghighi continues to bridge theoretical optimization with practical robotic control, offering valuable insights for students and researchers in autonomous systems and intelligent control.

Research Focus

Key Achievements

2
H-Index
3
Papers
23
Total Citations
8
Avg Citations/Paper
🏆 Most Cited Paper
Pareto optimal design of a fuzzy adaptive sliding mode controller for a three-link model of a biped robot via the multi-objective improved team game algorithm
11 citations · 2022
📈 Most Prolific Year: 2009 (2 Papers)
🤝 Key Collaborators: 7
🏛 Institutions: Islamic Azad University, Science and Research Branch, Sharif University of Technology

Top Papers

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  3. 3

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago