Jer-Nan Juang
Papers
9
Total Citations
296
H-Index
5
About
Jer-Nan Juang is a control systems engineer whose research has made significant contributions to robotics, iterative learning control (ILC), and system identification. His most influential work centers on developing and refining learning control methodologies that enable robotic systems to improve their precision over repeated task executions. His 2002 paper on zero-phase filtering applied to ILC, which has garnered over 150 citations, demonstrated how practically deployable learning controllers could be constructed for robotic tracking tasks by treating the problem as a two-dimensional system across time and repetition domains. Complementing this, his discrete frequency-based MIMO learning control framework provided a unifying theoretical foundation for convergence stability and transient behavior, accumulating 80 citations and influencing subsequent precision motion control research. Juang's earlier work in the 1990s compared multiple learning control architectures experimentally, establishing benchmarks that guided the field's development. He has also addressed fault tolerance in robotic systems using accelerometer-based sensor redundancy, path optimization to minimize base reaction forces in space environments, and, more recently, parameter identification for nonlinear robots subject to quantization error. Spanning over three decades, his research reflects a sustained commitment to making robotic control systems more reliable, precise, and theoretically well-grounded.
Research Focus
Key Achievements
Top Papers
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- 2Discrete frequency based learning control for precision motion control80 citations · 2002
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