Online Reduced-Order Data-Enabled Predictive Control
<|reference_start|>Online Reduced-Order Data-Enabled Predictive Control: Data-enabled predictive control (DeePC) has garnered significant attention for its ability to achieve safe, data-driven optimal control without relying on explicit system models. Traditional DeePC methods use pre-collected input/output (I/O) data to construct Hankel matrices for online predictive control. However, in systems with evolving dynamics or insufficient pre-collected data, incorporating real-time data into the DeePC framework becomes crucial to enhance control performance. This paper proposes an online DeePC framework for time-varying systems (i.e., systems with evolving dynamics), enabling the algorithm to update the Hankel matrix online by adding real-time informative signals. By exploiting the minimum non-zero singular value of the Hankel matrix, the developed online DeePC selectively integrates informative data and effectively captures evolving system dynamics. Additionally, a numerical singular value decomposition technique is introduced to reduce the computational complexity for updating a reduced-order Hankel matrix. Simulation results on two cases, a linear time-varying system and the vehicle anti-rollover control, demonstrate the effectiveness of the proposed online reduced-order DeePC framework.<|reference_end|>
@article{vahidi-moghaddam2024online,
    title={Online Reduced-Order Data-Enabled Predictive Control},
    author={Amin Vahidi-Moghaddam, Kaixiang Zhang, Xunyuan Yin, Vaibhav
  Srivastava, and Zhaojian Li},
    journal={arXiv preprint arXiv:2407.16066},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16066},
    primaryClass={eess.SY cs.SY}
}