Afzal Sikander

Soft Computing Approach for Model Order Reduction of Linear Time Invariant Systems

Most of the physical systems can be represented by mathematical models. The mathematical procedure of system modeling often leads to a comprehensive description of a process in the form of higher-order differential equations which are difficult to use either for analysis or for controller synthesis. It is, therefore, useful and sometimes necessary to find the possibility of some equations of the same type but of lower order that may be considered to adequately reflect almost all essential characteristics of the system under consideration. This paper proposes a new method for order reduction of higher-order linear time invariant systems based on stability equation method and particle swarm optimization algorithm. Reduced-order model will definitely be stable if the original model is stable. The superiority of the proposed method is illustrated by numerical examples of single-input, single-output systems and multiple-input and multiple-output systems. The results are compared with well-known methods available in the literature.

A Novel Order Reduction Method Using Cuckoo Search Algorithm

ABSTRACT In this paper, a novel method is presented to obtain stable reduced-order model of higher order linear time invariant continuous systems using optimization technique. In order to perform optimization, a new meta-heuristic search algorithm, called Cuckoo Search (CS) is applied which is inspired by the natural behaviour of cuckoo species including the Lévy flight behaviour of some birds and fruit flies. The effectiveness of the proposed method is supported by some numerical examples of single input single output (SISO) systems and the results are compared with other well-known methods available in the literature in terms of most popular performance indices like integral square error, integral absolute error, and integral time absolute error. The method is extended for multi input multi output (MIMO) systems also.

A New Technique For Reduced-Order Modelling of Linear Time-Invariant System

ABSTRACT In this paper, a new technique for order reduction of linear time-invariant systems is presented. This technique is intended for both single-input single-output (SISO) and multi-input multi-output (MIMO) systems. Motivated by other reduction techniques, the new proposed reduction technique is based on modified pole clustering and factor division algorithm with the objective of obtaining a stable reduced-order system preserving all essential properties of the original system. The new technique is illustrated by three numerical examples which are considered from the literature. To evaluate the superiority and robustness of the new technique, the results of the proposed technique are compared with other well-known and recently developed order-reduction techniques like Routh approximation and Big Bang-Big Crunch algorithm. The comparison of performance indices shows the efficiency and powerfulness of the new technique.

Reduced order modelling of linear time-invariant system using modified cuckoo search algorithm

In this paper modified cuckoo search (MCS) algorithm is considered to develop reduced order model (ROM) of higher-order linear time-invariant systems. Firstly, the MCS algorithm has been employed to minimize the integral square error (ISE) between original and proposed ROM to obtain its unknown coefficients. Five systems of different order are considered to obtain their reduced order model. Finally, various performance indices, such as ISE, integral of absolute and integral of time multiplied by absolute error, have been estimated to reveal the efficacy of the proposed model. Also, time and frequency response characteristics of original higher-order model are compared with the proposed MCS-based and some of other existing techniques-based ROM available in the literature. Furthermore, the results are compared in terms of time response specifications such as rise time (

A novel technique to design cuckoo search based FOPID controller for AVR in power systems

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Design of PID controller using cuckoo search algorithm for buck-boost converter of LED driver circuit

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A Comparative Analysis of PID Controller Design for AVR Based on Optimization Techniques

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