Model Order Reduction of Single Input Single Output Systems Using Artificial Bee Colony Optimization Algorithm

Bansal, Jagdish Chand; Sharma, Harish; Arya, K. V.

doi:10.1007/978-3-642-24094-2_6

Cited by 21 publications

(3 citation statements)

References 32 publications

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“…Wang et-al in [26]. A trend is followed by researchers for the implementation of optimization techniques in the MOR field [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] to find the unknown ROM. The numerator or denominator parameter in case of mixed-method [44] or to minimize the error between the original system response and reduced system response concluding a new reduced order transfer function.…”

Section: Introductionmentioning

confidence: 99%

A Novel Mixed Method Using MM-SE with Change in Routhain Array for Model Order Reduction

Sharma

Sambariya

2021

Preprint

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The system modelling leads towards the higher-order differential equations. These systems are difficult to analyse. Therefore, for ease and understanding, the conversion of higher to lower order is required. The model order reduction(MOR) is a systematic procedure to tackle these kinds of situations. This paper offers a mixed method for MOR using the modified moment matching (MM) and stability equation (SE). The modification is applied in the routhain array of MM. The approach has been verified by examining the error between the original, proposed and compared with reduced order available in the literature. The obtained result has been compared on the basis of step response characteristics and the response indices error.

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Section: Introductionmentioning

confidence: 99%

A Novel Mixed Method Using MM-SE with Change in Routhain Array for Model Order Reduction

Sharma

Sambariya

2021

Preprint

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“…Subsequently, some mixed methods were developed using Eigen spectrum and Pade approximation [10], Factor division algorithm and Eigen spectrum analysis [11] etc. Some evolutionary techniques [12][13][14] were also developed for order reduction of complex systems. In the present work, Krylov subspace projection is investigated for designing a reduced order suboptimal controller.…”

Section: Introductionmentioning

confidence: 99%

Reduced order suboptimal linear quadratic controller using Krylov subspace projection

Mehrotra

Chandra

2015

2015 39th National Systems Conference (NSC)

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The paper presents a method of designing a suboptimal linear quadratic controller for the reduced order approximant of a higher order system. The optimal control design for higher order systems is complicated because of the reason that for optimal control feedback from all the state variables is required. In this work, Krylov subspace projection is used to obtain the suboptimal controller parameters, based on the use of only measurable states. Krylov subspace projection is one of the most efficient solutions for reducing the higher order systems. It defines a projection from state space of higher dimension to the state space of lower dimension. To present the utility of the proposed technique, 2 examples are presented with simulation results.

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“…Recently, machine intelligence and cybernetics are most well-liked field in which ABC algorithm has become a popular strategy. Bansal et al solved the model order reduction optimization problem of single input single output systems (Bansal, Sharma, & Arya, 2012).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Swarm Artificial Bee Colony Algorithm

Sharma

Bansal

Arya

et al. 2012

International Journal of Applied Evolutionary Computation

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Artificial Bee Colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over test problems as well as real world optimization problems. This paper presents an attempt to modify ABC to make it less susceptible to stick at local optima and computationally efficient. In the case of local convergence, addition of some external potential solutions may help the swarm to get out of the local valley and if the algorithm is taking too much time to converge then deletion of some swarm members may help to speed up the convergence. Therefore, in this paper a dynamic swarm size strategy in ABC is proposed. The proposed strategy is named as Dynamic Swarm Artificial Bee Colony algorithm (DSABC). To show the performance of DSABC, it is tested over 16 global optimization problems of different complexities and a popular real world optimization problem namely Lennard-Jones potential energy minimization problem. The simulation results show that the proposed strategies outperformed than the basic ABC and three recent variants of ABC, namely, the Gbest-Guided ABC, Best-So-Far ABC and Modified ABC.

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Model Order Reduction of Single Input Single Output Systems Using Artificial Bee Colony Optimization Algorithm

Cited by 21 publications

References 32 publications

A Novel Mixed Method Using MM-SE with Change in Routhain Array for Model Order Reduction

A Novel Mixed Method Using MM-SE with Change in Routhain Array for Model Order Reduction

Reduced order suboptimal linear quadratic controller using Krylov subspace projection

Dynamic Swarm Artificial Bee Colony Algorithm

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