High-Performance Distributed Control for Large-Scale Linear Systems: A Partitioned Distributed Observer Approach
CoRR(2024)
摘要
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
local observer dimension is greatly reduced by 90
between the performance of the distributed control law and that of the
centralized control law is less than 1
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