Datengetriebene Regelungstechnik für Robotics
Model-based control approaches require the mathematical model of system to be controlled, which have been widely used in many applications. However, with the increasing complexity of system dynamics, modeling process by first principles has become more difficult. When the model is inaccurate, model-based control methods would lose the utility. As data is becoming more readily available, learning from data has then tracked more and more attentions in the control community. In our research group, we develop a new data-driven method for nonlinear system, which will be implemented on the different robotic platforms.
- W. Ye, P. Zhang, Y. Wang. Data-driven time-delayed control for Euler-Lagrange systems. Proceedings of the6th IEEE Conference on Control Technology and Applications (CCTA), pp. 926-931, Trieste, Italy, 2022.
- W. Ye, P. Zhang. Universal residual generator for nonlinear Euler-Lagrange systems. Proceedings of the 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), pp. 342-347, Pafos, Cyprus, 2022.
- Y. Wang, M. Leibold, J. Lee, W. Ye, J. Xie, M. Buss. Incremental Model Predictive Control for a Robot Manipulator: a Model-Free Approach. IEEE Transactions on Control Systems Technology. Vol. 30, No. 6, pp. 2285-2300, 2022.
- H-H. Chang. Implementation of data-driven control for aerial manipulation systems. 2023.
- T.H. Wang. Implementation of data-driven control for hexacopters. 2021.
- E. Wagner. Optimal control of unknown nonlinear underactuated Euler-Lagrange Systems. 2023.
- S. Bhatta. Incremental Model Predictive Control for Aerial Manipulators in task space. 2022.
- H. Chang. Implementation of position system for hexacopters. 2022.
- D. Hallerbach and J. Kickertz. A comparison of different data driven controller design approaches. 2020.