A comparative study of Differential Evolution and Simulated Annealing for order reduction of large scale systems

Saraswat, Princy; Parmar, Girish

doi:10.1109/ccintels.2015.7437923

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…40,41 For this reason, order reduction or linearization techniques may be employed. [42][43][44][45][46][47][48] In Refs. 26,27, we investigated the use of linear input-output approximations of the P2D model for the development of MPC strategies.…”

Section: Models For Controlmentioning

confidence: 99%

Design of Piecewise Affine and Linear Time-Varying Model Predictive Control Strategies for Advanced Battery Management Systems

Torchio¹,

Magni²,

Braatz³

et al. 2017

J. Electrochem. Soc.

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Advanced Battery Management Systems (ABMSs) are necessary for the optimal and safe operation of Li-ion batteries. This article proposes the design of ABMSs based on Model Predictive Control (MPC). In particular, we consider MPC strategies based on piecewise affine approximations (PWAs) of a first-principles electrochemical battery model known in the literature as the pseudo two-dimensional (P2D) model. The use of model approximations is necessary since the P2D model is too complex to be included in the real-time calculations required by MPC. PWAs allow to well describe the electrochemical phenomena occurring inside the battery. On the other side, the accuracy of such models increases with the number of considered partitions, which also increases the model complexity and the online computational cost of MPC. Linear time-varying (LTV) approximations, which are obtained by linearizing accurate PWAs around a nominal trajectory, are proposed as a way to further reduce online computational costs. The obtained results demonstrate the suitability of MPC based on PWARX and LTV model approximations to provide ABMSs with high performance.

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Section: Models For Controlmentioning

confidence: 99%

Design of Piecewise Affine and Linear Time-Varying Model Predictive Control Strategies for Advanced Battery Management Systems

Torchio¹,

Magni²,

Braatz³

et al. 2017

J. Electrochem. Soc.

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show abstract

“…Over many years, various techniques for model order reduction have been invented for simplification such as; system simplification techniques based on balanced truncation [1], whale optimization algorithm [2,6], combined techniques like: Eigen Spectrum Analysis (ESA) & Pade Approximation [3], ESA & Factor Division [4] and Eigen Permutation & Improved Pade Approximant (IPA) [7], different evolutionary algorithms like: Particle Swarm Optimization (PSO) [5] and Invasive Weed Optimization (IWO) [15]. Further, numerous techniques carried out by the researchers all around the world are available for MOR such as; design of control systems based on LOS [8], comparative analysis of different techniques [9], mixed method [10], Cuckoo Search optimization [11], Big Bang-Big Crunch (BB-BC) algorithm [12,14], soft computing techniques [16][17], Modified Cuckoo Search (MCS) algorithm [18], Stochastic Fractal Search (SFS) [20], the Routh Approximation [21], Stability Equation [22], differentiation [23], Response Matching Techniques [24], Pole Clustering [25] methods and many more [34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

WOA based Reduced Order Modeling of LTIC Systems

Kumar¹

2020

IJATCSE

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In this paper, the Whale Optimization Algorithm (WOA) has been employed in reduced order modeling of higher order linear time invariant continuous (LTIC) single-input single-output (SISO) systems. The algorithm has considerable features such as; superior performance, fast convergence, trouble-free implementation and gives numerically stable system. The strength of WOA has been tested on three types of systems having complex roots, repeated poles and also on one of the practical IEEE type 1 (DC) excitation model of 4 th order. In the present work, WOA minimizes the Integral Square Error (ISE) as an objective function to obtain all the gain parameters of numerator and denominator's polynomials of low order system (LOS). A comparative study has also been included to compare the proposed technique with some of famous/recent techniques existing in the literature for order reduction on the basis of ISE, IAE, IRE and various transient response parameters. The simulation results have been provided in graphical as well as tabular forms.

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A Novel Model-Order Diminution Algorithm for LTIC System Using Whale Optimization

Kumar

Parmar

Sikander

2022

Lecture Notes in Electrical Engineering

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A comparative study of Differential Evolution and Simulated Annealing for order reduction of large scale systems

Cited by 8 publications

References 19 publications

Design of Piecewise Affine and Linear Time-Varying Model Predictive Control Strategies for Advanced Battery Management Systems

Design of Piecewise Affine and Linear Time-Varying Model Predictive Control Strategies for Advanced Battery Management Systems

WOA based Reduced Order Modeling of LTIC Systems

A Novel Model-Order Diminution Algorithm for LTIC System Using Whale Optimization

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