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.

Veröffentlichungen:

• W. Ye, P. Zhang, H. Chang. A data-driven optimal time-delayed control approach and its application to aerial manipulators. Control Engineering Practice, Vol. 142, 105754, 2024.
• W. Ye, P. Zhang. Data driven adaptive control for unknown underactuated Euler-Lagrange system.  Proceedings of the 2024 European Control Conference (ECC), Stockholm, Sweden, 2024.
• J. Wang, P. Zhang, Y. Wang, Z. Ji. Data-driven adaptive dynamic programming for nonlinear systems with state and input constraints. Proceedings of the 10th International Conference on Control, Decision and Information Technologies (CoDIT), Valetta, Malta, 2024.
• 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.

Masterarbeiten:

• M. Memmer. A Data-Driven Approach for Trajectory Tracking Control of a Lightweight Robot Manipulator. 2024
• A. Eraslan. Data-Driven Model Predictive Control of Unknown Nonlinear Underactuated Euler-Lagrange System. 2024
• H-H. Chang. Implementation of data-driven control for aerial manipulation systems. 2023.
• T.H. Wang. Implementation of data-driven control for hexacopters. 2021.

Masterprojektarbeiten:

• M. Memmer. Design and implementation of a robotic arm for an aerial manipulator.2023
• A. Eraslan. Real Time Implementation of Incremental Model Predictive Control on Euler-Lagrange Systems. 2023
• 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.

Bachelorarbeiten:

• Z. Zhang. Adaptive Control Design for Precision Trajectory Tracking in UAV and Robot Manipulator Systems. 2023.
• J. Zhu. Tracking control for hexacopter systems in GBSS-denied environments. 2023.
• F. Zhao. Implementation of incremental model predictive control for hexacopters. 2022.
• X. Gu. Modellierung und Identifikation für Hexacopter. 2022.
• S. Khelil. Aufschwingungen von invertem Pendel mit Reinforcement Learning Methode. 2019.