Daniel Chifisi
Papers
1
Total Citations
5
H-Index
1
About
Daniel Chifisi is a rising robotics researcher whose work sits at the intersection of control theory, neural networks, and human-robot interaction. His primary research focuses on developing advanced control architectures for robotic manipulators, particularly through force-impedance control and model predictive control (MPC) strategies. In his most-cited work, Chifisi introduces a novel position-based force-impedance controller for 2-DOF planar robots, leveraging a multilayer perceptron (MLP) neural network structured as a nonlinear autoregressive model with exogenous inputs (NARX) to accurately capture robot dynamics. This approach, which employs successive linearization of the neural network model for real-time MPC, represents a significant step toward more adaptive and compliant robotic systems. While his citation count is still growing—his top paper has garnered 5 citations—the work demonstrates a sophisticated integration of learning-based modeling with classical control, positioning him as a promising contributor to the field. Chifisi’s research holds particular relevance for applications requiring safe, force-sensitive manipulation, such as collaborative robotics and assistive technologies, where precise interaction with uncertain environments is critical.
Research Focus
Key Achievements
Top Papers
- 1