Soo Young Lee

Korea Advanced Institute of Science and Technology

Papers

Total Citations

H-Index

About

Soo Young Lee’s research focuses on intelligent control systems, particularly the integration of neural networks with classical and adaptive control architectures for complex, nonlinear systems like robotics. Her major contributions lie in developing hybrid control structures that combine feedforward and feedback mechanisms with neural learning capabilities. In her 2002 work, she proposed a self-tuning controller that integrates a PD controller, an inverse dynamics compensator, and a neural controller, using the system’s inverse Jacobian to train the neural component—an innovative approach for real-time adaptation. Her 2005 paper advanced this by introducing a feedforward/feedback neural control structure featuring a neural identifier and three distinct controller blocks: a computed torque generator, a PD controller, and a multilayer neural compensator. This architecture demonstrated robust learning and compensation capabilities, particularly in robotic applications. While her citation counts (2 each) reflect a niche but focused impact, her work represents foundational steps in bridging neural networks with traditional control theory. Lee’s research is notable for its practical, system-oriented design, offering valuable insights for engineers developing adaptive controllers for autonomous systems and industrial robotics.