High-Performance Distributed Control for Large-Scale Linear Systems: A Partitioned Distributed Observer Approach
<|reference_start|>High-Performance Distributed Control for Large-Scale Linear Systems: A Partitioned Distributed Observer Approach: In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance. However, the traditional distributed observer requires each local observer to reconstruct the state information of the whole system, which is unrealistic for large-scale scenarios. To fill this gap, this paper develops a greedy-idea-based large-scale system partition algorithm, which can significantly reduce the dimension of local observers. Then, the partitioned distributed observer for large-scale systems is proposed to overcome the problem that the system dynamics are difficult to estimate due to the coupling between partitions. Furthermore, the two-layer Lyapunov analysis method is adopted and the dynamic transformation lemma of compact errors is proven, which solves the problem of analyzing stability of the error dynamic of the partitioned distributed observer. Finally, it is proved that the distributed control law based on the partitioned distributed observer can also arbitrarily approximate the control performance of the centralized control law, and the dimension of the local observer is greatly reduced compared with the traditional method. The simulation results show that when the similarity between the physical network and the communication network is about 80%, the local observer dimension is greatly reduced by 90% and the relative error between the performance of the distributed control law and that of the centralized control law is less than 1%.<|reference_end|>
@article{xu2024high-performance,
    title={High-Performance Distributed Control for Large-Scale Linear Systems: A
  Partitioned Distributed Observer Approach},
    author={Haotian Xu and Shuai Liu and Ling Shi},
    journal={arXiv preprint arXiv:2402.06903},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06903},
    primaryClass={eess.SY cs.SY math.DS}
}