Papers
7
Total Citations
113
H-Index
4
About
Eva Portillo’s research focuses on the control, modeling, and sensing of parallel robots—high-precision mechanisms used in industry and rehabilitation. Her major contributions lie in integrating neural networks and redundant sensors to solve complex kinematic and dynamic problems in real time, significantly improving robot accuracy and stability. Her most-cited work, “Real time direct kinematic problem computation of the 3PRS robot using neural networks” (55 citations), demonstrates a novel approach to overcoming computational bottlenecks in parallel robot control. In “Redundant sensor based control of the 3RRR parallel robot” (30 citations), she shows how additional sensor data can reduce control errors, a theme she extends to rehabilitation robotics in “Virtual Sensors for Advanced Controllers in Rehabilitation Robotics” (13 citations), where she addresses the challenge of measuring patient-robot interaction without costly hardware. Her work on redundant dynamic modeling and the stable Extended CTC control scheme further underscores her commitment to practical, high-performance robotic systems. With over 110 citations across her key papers, Portillo’s research bridges theoretical advances and real-world applications, offering valuable insights for students and engineers working on parallel robot control, sensor integration, and assistive robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Redundant sensor based control of the 3RRR parallel robot30 citations · 2012
- 3Virtual Sensors for Advanced Controllers in Rehabilitation Robotics13 citations · 2018
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- 5
- 6
- 7Extended CTC control for parallel robots2 citations · 2010