Papers
16
Total Citations
121
H-Index
7
About
Patrice Wira is a prominent researcher whose work spans robotics, autonomous systems, and advanced control theory, with particular expertise in nonlinear optimal control and neural network-based learning for complex robotic applications. His career reflects a compelling evolution from neural computation to sophisticated control engineering, bridging intelligent systems with real-world robotic challenges. Early in his career, Wira made notable contributions to neural network architectures for robotics, developing modular self-organizing map (SOM) frameworks that enabled robots to learn complex visuo-motor correlations — work that garnered 15 citations and demonstrated the power of biologically inspired computing in spatial learning tasks. His subsequent research shifted toward rigorous control-theoretic approaches, producing influential solutions for autonomous truck-trailer systems using nonlinear H-infinity control (21 citations), electro-hydraulic robotic manipulators (13 citations), and lower-limb exoskeletons for rehabilitation (9 citations). His flatness-based control methodology, applied to both industrial and mobile robots, further demonstrates his versatility across platforms. Collectively, his publications address critical challenges in autonomous navigation, power infrastructure inspection, and human-assistive robotics. With over 100 cumulative citations across his most recognized works, Wira's research continues to shape the intersection of optimal control theory and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4Neural Networks Organizations to Learn Complex Robotic Functions11 citations · 2003
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- 9Non-linear optimal control for four-wheel omnidirectional mobile robots5 citations · 2020
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