Chee Pin Tan
Papers
16
Total Citations
386
H-Index
10
About
Chee Pin Tan is a distinguished researcher whose work spans advanced control theory, state estimation, and soft robotics — fields where his contributions have meaningfully shaped both theoretical foundations and practical applications. His early work on sliding-mode observers, including his widely cited 2010 paper on terminal sliding mode observers for nonlinear systems (154 citations), established him as a leading voice in robust observer design, a critical area for controlling complex dynamical systems with incomplete information. Over time, Tan expanded his expertise into the rapidly evolving domain of soft robotics, addressing one of the field's most persistent challenges: reliable sensing in mechanically compliant systems. His group pioneered indirect sensing approaches that leverage neural networks, Kalman filtering, and H-infinity methods to estimate states without physically embedding sensors — work that has attracted significant attention, including over 60 citations for his 2021 multimodal sensing study. More recently, Tan has explored deep learning frameworks, predictive uncertainty quantification, and synthetic data generation for soft robots, demonstrating a forward-thinking adaptability. His research on fault estimation and remaining useful life prediction for industrial robots further underscores the breadth of his impact across both emerging and established robotics domains.
Research Focus
Key Achievements
Top Papers
- 1Terminal sliding mode observers for a class of nonlinear systems154 citations · 2010
- 2
- 3Sliding-Mode Observers25 citations · 2007
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- 8A Deep Learning Framework for Soft Robots with Synthetic Data13 citations · 2023
- 9
- 10A Robust Fault Estimation Scheme for a Class of Nonlinear Systems10 citations · 2016