Reduced Order Modelling of Higher-Order Systems by ICG Technique

The complexity of the control mechanisms is increasing with the developments occurring around the world. For the continuous monitoring of the complex systems, the order of the systems must be reduced. Hence, a model order abatement technique, enhanced clustering-plus-genetic (ICG) is presented in the paper. The technique is a merger of two techniques; one is improved pole clustering and second is genetic algorithm. An enhanced pole clustering technique lay-out the denominator while genetic algorithm succours to procure the numerator of reduced-order system. The technique proposed in the paper has the main feature that reduced-order system have the effect of all poles, existing in the higher-order system are involved in deriving the poles of reduced-order system. ICG MOR technique is applied to the single variable continuous-time as well as discrete-time system. The potency of the evolved method is checked by comparing the time and frequency domain performance characteristics of higher-order system with the value of parameters for reduced-order system and error values like integral absolute error (IAE), integral time absolute error (ITAE) and integral square error (ISE). To study the supremacy of the ICG technique, its comparison is executed with other techniques developed in recent years.