Edgar Ademir Morales Perez
Papers
1
Total Citations
3
H-Index
1
About
Edgar Ademir Morales Perez is a researcher at the forefront of intelligent robotic control systems, specializing in the integration of deep learning and predictive optimization for complex, non-linear dynamics. His most cited work, "LSTM Neural Network-based Predictive Control for a Robotic Manipulator" (2021), introduces a novel framework that leverages Long Short-Term Memory networks combined with Differential Evolution optimization to achieve high-accuracy control of robotic manipulators. This approach addresses the fundamental challenge of managing multiple-input, multiple-output systems with highly non-linear behavior, offering a robust alternative to traditional control methods. With 3 citations, this paper has already sparked interest in the robotics and control communities for its practical potential in real-time applications. Morales Perez’s contributions lie at the intersection of neural network modeling and model predictive control, advancing the precision and adaptability of autonomous robotic systems. His work is particularly valuable for students and researchers exploring how deep learning can enhance the performance of industrial and service robots, paving the way for more responsive and intelligent automation in manufacturing and beyond.
Research Focus
Key Achievements
Top Papers
- 1LSTM Neural Network-based Predictive Control for a Robotic Manipulator3 citations · 2021