Max Robotics — Global Service Robot Directory + US Certification

About

Juan Padron’s research centers on the control of two-inertia systems—the backbone of applications from industrial robots to electric vehicle drivetrains. His major contributions tackle the twin scourges of backlash and nonlinear friction, which cause limit cycles and instability in torsion torque control (TTC). Padron’s work uniquely bridges these phenomena, proposing stable TTC schemes that suppress gear impact and improve system robustness. His 2021 paper on “Stable Torsion Torque Control Based on Friction-Backlash Analogy” (4 citations) introduces a friction-backlash analogy to eliminate unstable behavior, while his follow-up on gear impact suppression (3 citations) provides a practical method for powertrain and robotics applications. In 2022, he systematically evaluated the equivalence between nonlinear friction and backlash (2 citations), offering a unified framework for compensation. Additionally, his work on equivalent disturbance compensators (EDC) for back-forward drivability (2 citations) advances load-side control in friction-affected systems. Though early in his career, Padron’s focused, high-impact contributions are shaping next-generation control strategies for safer, more responsive robotic and automotive systems.