Maycol de Alencar
Papers
4
Total Citations
18
H-Index
2
About
Maycol de Alencar is a researcher specializing in adaptive neural control for robotic systems, with a focus on trajectory tracking in the presence of uncertainties and disturbances. His work bridges kinematic and dynamic modeling for both wheeled mobile robots and robot manipulators. In his most cited paper (2015, 10 citations), he proposed an adaptive neural control integrating a kinematic neural controller and a torque neural controller to handle uncertainties and disturbances in wheeled mobile robot trajectory tracking. Earlier contributions include an adaptive neural network controller for robot manipulators in task space (2005, 4 citations), where he compared performance against passivity-based controllers under friction torques. His 2004 work (2 citations) further extended this by comparing adaptive neural control with computed-torque and passivity-based methods in joint space. De Alencar’s research is notable for systematically addressing real-world challenges like friction and disturbances, advancing robust control strategies for autonomous systems. Though his citation counts are modest, his work provides foundational insights into neural adaptive control for robotics, offering practical solutions for trajectory tracking in uncertain environments.
Research Focus
Key Achievements
Top Papers
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