A new approach for designing a reduced-order controller of linear singular systems

1990 ◽  
Vol 35 (4) ◽  
pp. 492-495 ◽  
Author(s):  
M.T.F. Saidahmed
Keyword(s):  
Singular Systems ◽  
New Approach ◽  
Reduced Order

Reduced-Order Observer's Design and Parameterization for Singular Systems

1998 ◽  
Vol 31 (25) ◽  
pp. 135-138
Author(s):  
Guo-Shan Zhang ◽  
Shi-Lian Yu ◽  
Tian-You Chai
Keyword(s):  
Singular Systems ◽  
Reduced Order

scholarly journals A Robust Computational Technique for Model Order Reduction of Two-Time-Scale Discrete Systems via Genetic Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Othman M. K. Alsmadi ◽  
Zaer S. Abo-Hammour
Keyword(s):  
Genetic Algorithms ◽  
Time Scale ◽  
Model Order Reduction ◽  
Fitness Function ◽  
Order Reduction ◽  
Discrete Systems ◽  
Computational Technique ◽  
Model Order ◽  
New Approach ◽  
Reduced Order

A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-input multioutput (MIMO)) is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA) with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements ofB,C, andDmatrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method is compared to recently published work on MOR techniques where simulation results show the potential and advantages of the new approach.

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