Maina Martin Ruthandi
Papers
1
Total Citations
5
H-Index
1
About
Maina Martin Ruthandi is a rising force in the field of robotics and intelligent control systems, with a focused expertise in the modeling and control of robot manipulators. His primary research areas include force-impedance control, model predictive control (MPC), and the application of neural networks for dynamic system identification. Ruthandi’s most notable contribution is his pioneering work on integrating neural network models with MPC for robotic control. In his highly cited 2024 paper, he developed a novel position-based force-impedance controller for a 2-DOF planar robot, employing a multilayer perceptron (MLP) neural network structured as a nonlinear autoregressive model with exogenous input (NARX) to accurately capture the robot’s complex dynamics. This approach, which uses successive linearization of the neural network model, enables precise and adaptive control in tasks requiring both force and position regulation. With 5 citations already, his work is gaining traction for its practical implications in advanced manufacturing and human-robot collaboration. Ruthandi’s research stands out for bridging the gap between data-driven modeling and real-time control, offering a scalable solution for next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
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