Hou Linqi

Papers

Total Citations

H-Index

About

Hou Linqi’s research career centers on advancing intelligent control systems for robotics, with a particular focus on pneumatic and force-based applications. His early work pioneered the integration of online learning neural networks into pneumatic robot position control, demonstrating how adaptive algorithms can compensate for system nonlinearities and time-varying parameters—a critical step toward more robust industrial automation. This foundational paper, though from 2002, remains cited for its practical approach to real-time learning in hardware-constrained environments. Hou further contributed to precision manufacturing by developing adaptive feedforward control schemes for robotic deburring operations. His work on smooth tracking control, using ARMAX models identified via least squares, enabled accurate contact force regulation—directly addressing the challenge of maintaining performance under dynamic loads. While his citation counts (6 and 2 respectively) reflect a focused, niche impact, these contributions are valued by researchers working at the intersection of neural control and pneumatic actuation. Hou’s emphasis on online learning and adaptive compensation has informed subsequent developments in force-sensitive robotics, particularly in applications requiring both precision and adaptability. His legacy lies in demonstrating that neural controllers can be practically deployed in real-time robotic systems, bridging the gap between theoretical control methods and industrial implementation.