Renato De Leone
Papers
2
Total Citations
12
H-Index
2
About
Renato De Leone is a leading researcher in advanced robotics control, specializing in the challenging domain of flexible-joint robots operating under communication constraints. His primary research areas include adaptive control, prescribed performance control, and the critical issue of state quantization in robotic systems. De Leone’s major contributions address a fundamental problem: how to maintain precise tracking control when all system states—position, velocity, and joint torque—are subject to the discontinuities of uniform quantization. In his highly cited 2024 work (9 citations), he pioneered a command-filtered adaptive control approach that overcomes the chattering and instability caused by quantized feedback. Building on this, his 2025 study (3 citations) introduced a groundbreaking low-complexity prescribed performance scheme, ensuring that tracking errors converge to a pre-defined, arbitrarily small residual set even under severe quantization. Notably, this work includes experimental validation, bridging the gap between theory and real-world application. De Leone’s research is vital for the next generation of networked and resource-constrained robotic systems, offering robust, implementable solutions that guarantee both stability and performance.
Research Focus
Key Achievements
Top Papers
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