Papers
4
Total Citations
30
H-Index
4
About
Miguel Bernal’s research lies at the intersection of nonlinear control theory, robotics, and neural networks, with a focus on trajectory tracking for complex mechanical systems. His major contributions include extending computed-torque control to parallel robots for applications in ankle reeducation (2019, 13 citations), and developing differential algebraic observer-based trajectory tracking techniques that estimate joint velocities from position data alone using linear matrix inequalities (2022, 4 citations). Bernal has also advanced adaptive control by designing dynamic neural network-based controllers that eliminate modeling errors (2003, 4 citations), and addressed nonholonomic mobile robot trajectory tracking through extended models (2011, 9 citations). His work is characterized by rigorous Lyapunov-based stability proofs and practical implementations for rehabilitation and mobile robotics. With a career spanning two decades, Bernal’s research has been published in international venues and cited by peers working on parallel manipulators, observer design, and neural control. His 2019 paper on ankle reeducation highlights the translational potential of his work, bridging theoretical control advances with real-world medical devices.
Research Focus
Key Achievements
Top Papers
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- 4Nonlinear system adaptive trajectory tracking by dynamic neural control4 citations · 2003