Ying‐Cheng Lai

Arizona State University, Kyungpook National University

Papers

Total Citations

H-Index

About

Ying-Cheng Lai is a pioneering figure in nonlinear dynamics, complex systems, and control theory, whose work bridges fundamental physics and real-world engineering. His research spans chaos synchronization, network science, and machine learning for control, with a particular focus on enabling robust, model-free tracking in complex dynamical systems. In his highly cited 2023 paper (67 citations), Lai introduced a model-free tracking control framework using machine learning, eliminating the need for explicit system equations—a breakthrough with direct implications for robotics and autonomous systems. Earlier, his 2013 study (27 citations) revealed a counterintuitive strategy for achieving synchronization in moving-agent networks by restricting interactions, offering scalable solutions for sensor networks and swarm robotics. Lai also explores the control of "complex objects"—systems with internal degrees of freedom, such as a cup of coffee held while walking—addressing fundamental gaps in human-robot interaction and prosthetics. With over 400 publications and an h-index exceeding 80, his work is widely recognized for its theoretical depth and practical impact. A Fellow of the American Physical Society and IEEE, Lai continues to shape the frontiers of nonlinear science and intelligent control.