Andres Pizarro-Lerma
Papers
5
Total Citations
38
H-Index
4
About
Andres Pizarro-Lerma is a robotics control researcher whose work focuses on advancing trajectory tracking for robot manipulators through novel hybrid control architectures. His primary research areas include fuzzy logic control, adaptive neural networks, and feedforward compensation techniques for robotic systems. Pizarro-Lerma’s major contribution lies in developing and experimentally validating sectorial fuzzy controllers—a specialized form of fuzzy logic control—integrated with adaptive neural network compensation and feedforward dynamics. His most cited work, “Fine-Tuning of a Fuzzy Computed-Torque Control for a 2-DOF Robot via Genetic Algorithms” (2018, 18 citations), demonstrates how genetic algorithms can optimize fuzzy membership functions to enhance controller performance. In subsequent papers, including “Experimental Evaluation of a Sectorial Fuzzy Controller Plus Adaptive Neural Network Compensation” (2019, 8 citations) and “Sectorial Fuzzy Controller Plus Feedforward for the Trajectory Tracking of Robotic Arms” (2021, 7 citations), he provides rigorous stability proofs via Lyapunov criteria and validates his approaches through both simulation and real-time experiments. His 2025 paper introduces a new motion tracking controller that outperforms previous schemes, showcasing his ongoing commitment to improving robotic precision and reliability. Pizarro-Lerma’s work bridges theoretical control design with practical implementation, offering valuable insights for students and researchers in robotics and intelligent control systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5