Francisco Jurado
Instituto Tecnólogico de La Laguna, Tecnológico Nacional de México
Papers
9
Total Citations
90
H-Index
6
About
Francisco Jurado is a leading researcher in intelligent control systems, with a primary focus on decentralized neural control, real-time identification, and nonlinear trajectory tracking for robotic manipulators. His major contributions lie in the development of novel continuous-time neural architectures, including Recurrent High-Order Neural Networks (RHONN) and Recurrent Wavelet First-Order Neural Networks (RWFONN), which enable robust, real-time decentralized control for complex robotic systems. His work has been widely cited—his most influential paper, “Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors” (2016), has garnered 23 citations, while his wavelet-based neural control studies have accumulated over 30 citations collectively. Beyond manipulators, Jurado has advanced control strategies for underactuated systems, such as ballbot robots, employing Takagi–Sugeno fuzzy controllers and discrete-time linear quadratic regulators. His research bridges theoretical neural control with practical, real-time implementation, making significant strides in autonomous robotics and nonlinear system identification.
Research Focus
Key Achievements
Top Papers
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- 8Discrete–Time Linear Quadratic Regulator for a NXT Ballbot System5 citations · 2022
- 9Continuous-time neural control for a 2 DOF vertical robot manipulator4 citations · 2011