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Journal Articles
- 2021
[j2]Shengwen Xie
, Juan Ren:
Tracking Control Using Recurrent-Neural-Network-Based Inversion Model: A Case Study on a Piezo Actuator. IEEE Trans. Ind. Electron. 68(11): 11409-11419 (2021)- 2020
- [j1]Shengwen Xie, Juan Ren:
Linearization of Recurrent-Neural-Network- Based Models for Predictive Control of Nano-Positioning Systems Using Data-Driven Koopman Operators. IEEE Access 8: 147077-147088 (2020)
Conference and Workshop Papers
- 2021
- [c7]Shengwen Xie, Juan Ren:
A Hybrid Position/Force Controller for Joint Robots. ICRA 2021: 6415-6421 - 2020
- [c6]Shengwen Xie, Juan Ren:
Discrete System Linearization using Koopman Operators for Predictive Control and Its Application in Nano-positioning. AIM 2020: 1374-1379 - [c5]Shengwen Xie, Juan Ren:
Towards Improving the Performance of the RNN-based Inversion Model in Output Tracking Control. ACC 2020: 4301-4306 - [c4]Shengwen Xie, Sia M. L. Andersson, Shengwei Ye, Zhichuan Niu, John H. Marsh, Lianping Hou:
Design of 2 μm Wavelength Polarization Mode Controllers. UCET 2020: 1-3 - 2019
- [c3]Shengwen Xie, Juan Ren:
Recurrent-neural-network-based Predictive Control of Piezo Actuators for Precision Trajectory Tracking. ACC 2019: 3795-3800 - [c2]Shengwen Xie, Juan Ren:
Predictive Control of Nano-positioning Stage Using Recurrent-neural-network-based Inversion Model. CDC 2019: 7764-7769 - 2018
- [c1]Shengwen Xie, Juan Ren:
Iterative Learning-based Model Predictive Control for Precise Trajectory Tracking of Piezo Nanopositioning Stage. ACC 2018: 2922-2927
Informal and Other Publications
- 2020
- [i2]Shengwen Xie, Juan Ren:
Towards Improving the Performance of the RNN-based Inversion Model in Output Tracking Control. CoRR abs/2001.00058 (2020) - [i1]Shengwen Xie, Juan Ren:
A Hybrid Position/Force Controller for Joint Robots. CoRR abs/2010.15350 (2020)
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