Maedeh Taj
Papers
1
Total Citations
19
H-Index
1
About
Maedeh Taj is a control systems researcher whose work focuses on the complex dynamics of multi-agent systems, particularly in the presence of uncertainty and physical constraints. Her primary research areas include adaptive control, distributed consensus algorithms, and neural network-based approximation for nonlinear systems. Taj’s most cited work, a 2019 study on distributed adaptive consensus tracking control, addresses a critical challenge in multi-agent coordination: ensuring stability and performance when agents face unknown control gains and input saturation. By integrating radial basis function neural networks to approximate uncertain dynamics, she proposed a novel scheme that allows follower agents to track a leader’s trajectory reliably, even under actuator limitations. This contribution has garnered 19 citations, reflecting its relevance to advancing autonomous systems, such as drone swarms and robotic teams. Taj’s research is notable for bridging theoretical rigor with practical constraints, offering solutions that are both mathematically sound and implementable in real-world applications. Her work continues to influence the development of resilient, decentralized control strategies for next-generation autonomous networks.
Research Focus
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Top Papers
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