M. S. Santana
Papers
4
Total Citations
15
H-Index
2
About
M. S. Santana is a researcher at the intersection of control theory, mobile robotics, and engineering education. Their primary contributions lie in developing and implementing neural network-based controllers—specifically using the Group Method of Data Handling (GMDH)—for mobile robot obstacle following and avoidance. Santana’s most cited work (2023, 9 citations) demonstrates the hardware implementation of a GMDH controller as a more efficient alternative to traditional PID controllers for non-linear systems, validated through real-world mobile robot applications. This work is complemented by a comparative study (2025, 2 citations) evaluating GMDH against Perceptron neural controllers with hardware-in-the-loop validation. Beyond technical contributions, Santana is pioneering innovative pedagogical approaches to control theory education. Their recent publications (2024–2025) integrate STEM education, mobile robot simulation, and bio-inspired optimization into the classroom, proposing face-to-face and virtual laboratory methodologies that enhance student engagement with complex control systems. With a growing citation footprint and a dual focus on advancing both control algorithms and educational practices, Santana is establishing themselves as a forward-thinking contributor to robotics and engineering pedagogy.
Research Focus
Key Achievements
Top Papers
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