Nonlinear Model Learning for Compensation and Feedforward Control of Real-World Hydraulic Actuators Using Gaussian Processes
Link to Published Article → IEEE Robotics and Automation Letters, 2022, Volume 7, Issue 4.
Video
Highlights
- Data-driven modeling of actuator flow characteristics using GPs
- Feedforward controller training framework integrating GP models.
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Machine control with Python over CAN interface (Codes:
CANAutomate)
Presentation at 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) in Kyoto, Japan
Citation
Abdolreza Taheri et al. “Nonlinear Model Learning for Compensation and Feedforward Control of Real-world Hydraulic Actuators using Gaussian Processes”. In: IEEE Robotics and Automation Letters 7.4 (2022), pp. 9525-9532. DOI: 10.1109/LRA.2022.3190808. Presented at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) in Kyoto, Japan.