Papers
14
Total Citations
228
H-Index
5
About
Pierre Sicard is a control systems researcher whose work centers on the dynamics and intelligent control of robotic manipulators, with particular emphasis on flexible-joint systems, friction compensation, and adaptive control strategies. Over more than two decades, Sicard has made sustained contributions to one of robotics' most challenging problems: designing robust controllers for systems whose mechanical compliance and nonlinear friction make precise motion control exceptionally difficult. His most influential contribution, "ANN-Based Adaptive Control of Robotic Manipulators With Friction and Joint Elasticity" (2009, 152 citations), demonstrated how artificial neural networks with variable learning rates could effectively compensate for complex, nonlinear joint dynamics — a result widely adopted by the robotics control community. Building on this foundation, Sicard has explored hybrid neural-fuzzy sliding mode architectures, passivity-based and Lyapunov stability methods, and variable structure observers that reconstruct unmeasured system states — work with practical implications for cost-effective robotic implementation. His research portfolio also extends to real-world applications, including a passivity-based approach applied to the Space Shuttle Remote Manipulator System, and more recently to stabilization of nonholonomic hopping robots. Collectively, Sicard's body of work reflects a rigorous, mathematically grounded approach to making intelligent robotic control reliable in the presence of uncertainty, friction, and structural flexibility.
Research Focus
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Top Papers
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- 8Variable structure observer design for flexible joint manipulators4 citations · 1997
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