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

Abstract

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 of B, C, and D matrices. 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.

Routh--type $L_2$ model reduction revisited

“…The model reduction problem has aroused a continual interest in the engineering community since the dawn of control and system theory [40,71], its importance being evident not only in system simulation and controller synthesis but also in many problems related to robustness and uncertainty issues. Indeed, despite the dramatic increase of computing capabilities that make the need for simplified models less compelling, the new challenges facing the control engineer have led to a revival of studies on this topic with particular emphasis on optimisation and algorithmic efficiency (see, e. g., [1,2,4,7,11,12], [14]- [59], [61,65,69,70]).…”

Reduced Order Model of Single Machine Infinite Bus Power System Using Stability Equation Method and Self-adaptive Bat Algorithm

“…The application of particle swarm optimization (PSO) is examined in [25] by minimizing integral of square error of the response of original and reduced system. The genetic algorithm (GA) have been considered to determine the free coefficients of numerator and denominator of discrete transfer function in [28]. Soloklo have presented the application of harmony search algorithm with multi-objective function for determining the lower order model of HO systems [29].…”

“…e (s) = 2000s 6 + 1211000s 4 + 58270000s 2 th order reduced order numerator polynomial is given in Eqn 28…”