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Model Order Reduction Based on Dynamic Relative Gain Array for MIMO Systems
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DOI：10.1109/TCSII.2019.2962709.png)
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Abstract
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The computational efficiency of traditional model order reduction (MOR) methods may degrade sharply for multi-input multi-output (MIMO) systems especially when the number of ports of MIMO systems is very large. During the concrete computation process, many input-output pairs can be ignored due to the weak interactions to each other, and hence the efficiency of reduction can be improved by reducing the number of ports. In this brief, we develop a dynamic relative gain array (DRGA) method to decide which inputs are important enough to an output in the MOR process. The DRGA method is based on the state feedback predictive control, and both the steady state information and the dynamic information are considered in the process of loop pairing. Multi-input single-output (MISO) subsystems can be obtained from decoupling the original large MIMO system. Experimental results on RLC networks show that the proposed DRGA based MOR method has higher accuracy compared with the passive reduced-order interconnect macromodeling (PRIMA) method, the decentralized model order reduction (DeMOR) method, and the balance truncation reduction (BTR) method.
Keywords:
MIMO communication
Steady-state
Integrated circuit modeling
Computational modeling
Predictive control
Transfer functions
State feedback
MIMO systems
model order reduction
dynamic relative gain array
RLC networks
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