Data-driven control includes several control theories and methods where controllers are designed directly using online or offline input/output (I/O) data of the controlled system or knowledge obtained from data processing, without relying on mathematical models and any explicit information about the controlled process structure. Developing data-driven algorithms that can directly learn from (I/O) data and construct control laws will generate new learning-based control algorithms that can maintain consistent performance even in the presence of complex unstructured uncertainties and dynamic changes.

Representative Topics

• Structure-independent control
• Uncertain Euler-Lagrange systems
• Adaptive sliding mode control
• Relaxation of persistent excitation conditions
• Non-falsification control

Representative Applications

• Unstructured robotic control (e.g., reconfigurable robots)
• Decision-making in unstructured environments
• Iterative feedback tuning
• Simultaneous Localization and Mapping (SLAM)

Representative Publications

• Roy S. and Baldi S., “Towards structure-independent stabilization for uncertain underactuated Euler-Lagrange systems”. Automatica, Vol. 111, art. 108775, 2020. doi:10.1016/j.automatica.2019.108775
• Baldi S., Michailidis I., Kosmatopoulos E. B., Papachristodoulou A., and Ioannou P. A., “Convex design control for practical nonlinear systems”. IEEE Transactions on Automatic Control, Vol. 59(7), pp. 1692-1705, 2014. doi:10.1109/TAC.2014.2309271
• Baldi S., Michailidis I., Kosmatopoulos E. B., and Ioannou P. A., “A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation”. IEEE Control Systems Magazine, Vol. 34(5), pp. 56-71, 2014. doi:10.1109/MCS.2014.2333272