Sergio Escalera Canto
Papers
1
Total Citations
2
H-Index
1
About
Sergio Escalera Canto is a researcher whose work bridges classical control theory and modern adaptive systems, with a primary focus on precision motor control and embedded system optimization. His most cited study, "Speed Control of DC Servo Motor Under Comparison with PID Tuner Control and Neural Network Control Using Simulink and ESP32," demonstrates a rigorous comparative analysis of PID tuning and neural network-based methods for DC servomotor regulation. By applying Kirchhoff’s law to model the TRE-TS3252 motor’s excitation dynamics, Escalera Canto shows how adaptive control can outperform traditional PID approaches in real-time feedback systems, particularly when implemented on low-cost microcontrollers like the ESP32. This work, with 2 citations, has practical implications for robotics, automation, and educational platforms, offering engineers a clear methodology for selecting control strategies. His contributions highlight the synergy between simulation tools (Matlab Simulink) and hardware deployment, making advanced control accessible for prototyping. Escalera Canto’s research is especially valuable for students and practitioners seeking to understand the trade-offs between classical and neural control in resource-constrained environments, positioning him as a practical innovator in mechatronics and embedded systems.
Research Focus
Key Achievements
Top Papers
- 1