Papers
4
Total Citations
54
H-Index
4
About
Matin Jafarian is a researcher whose work spans robotics, control systems, and intelligent automation, with particular expertise in multi-agent systems, nonholonomic dynamics, and model predictive control. His most recognized contributions lie in the domain of formation control for wheeled robotic networks, where he has developed robust frameworks for coordinating groups of nonholonomic wheeled robots in the presence of real-world disturbances. By leveraging the port-Hamiltonian framework, Jafarian's work on disturbance rejection in formation keeping — his most cited contribution with 27 citations — has provided theoretically grounded and practically relevant solutions for multi-robot coordination challenges. His 2015 companion paper further established the foundational methodology that underpins this line of research. Beyond multi-robot systems, Jafarian has made meaningful strides in advanced control theory, notably proposing an event-triggered model predictive control approach that integrates machine learning to compensate for model uncertainties, earning 16 citations and reflecting growing interest in intelligent control architectures. His earlier work on bipedal locomotion demonstrates a breadth of interest in robotic systems more broadly. Together, his research portfolio reflects a consistent commitment to making autonomous robotic systems more resilient, efficient, and practically deployable.
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