Andre Petronilho
Universidade Federal de São Carlos, Universidade de São Paulo
Papers
2
Total Citations
9
H-Index
2
About
Andre Petronilho’s research focuses on the intersection of adaptive control, neural networks, and nonlinear systems, with a particular emphasis on robotic manipulators operating under constraints and uncertainties. His major contributions lie in developing robust tracking control strategies that guarantee H∞ performance, effectively mitigating the impact of plant uncertainties and external disturbances. Notably, his 2006 paper on adaptive neural network tracking control for constrained robot systems, which has garnered 6 citations, proposes a novel approach that integrates neural networks to achieve superior disturbance attenuation. Earlier, in 2004, he compared three distinct nonlinear H∞ control techniques for robot manipulators, including an explicit solution derived from the dynamic parameter matrix, providing a foundational analysis that has been cited 3 times. While his citation counts reflect a focused and specialized impact, Petronilho’s work is recognized for advancing the theoretical and practical understanding of robust control in robotics, offering valuable insights for researchers developing high-performance, safety-critical robotic systems. His contributions are particularly relevant for applications requiring precise motion control under physical constraints and model uncertainties.
Research Focus
Key Achievements
Top Papers
- 1
- 2Adaptive nonlinear ℋ;/sub ∞/ techniques applied to a robot manipulator3 citations · 2004