Papers
9
Total Citations
176
H-Index
4
About
Teh-Lu Liao is a control systems researcher whose work spans robust nonlinear control, adaptive systems, and intelligent robotics. His most influential contributions emerged in the early 1990s, when he developed foundational frameworks for robust and adaptive tracking of nonlinear systems using variable structure control (VSC) and input-output linearization techniques. His 1990 paper on globally stable robust tracking — which has garnered 112 citations — remains his landmark achievement, demonstrating how VSC laws could guarantee stable output tracking in nonlinear systems with bounded uncertainties, with direct applications to robotic manipulators. A companion adaptive approach eliminated the need for prior knowledge of uncertainty bounds, further broadening practical applicability. Throughout the 1990s, Liao extended these ideas into neural network-based control, employing CMAC networks and radial basis function neural networks for adaptive output tracking of unknown MIMO nonlinear systems. In later years, his research diversified into multi-robot formation control using distributed sliding-mode strategies, cube robot dynamics and balancing, and computer vision-driven tracking systems incorporating convolutional neural networks and PID control. Collectively, his body of work bridges classical nonlinear control theory with modern intelligent systems, offering enduring relevance across robotics, autonomous vehicles, and real-time control applications.
Research Focus
Key Achievements
Top Papers
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- 4Nonlinear Dynamics and Control of a Cube Robot5 citations · 2020
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