Girish Parmar

Bio: Girish Parmar is an academic researcher from Rajasthan Technical University. The author has contributed to research in topics: PID controller & Digital watermarking. The author has an hindex of 14, co-authored 82 publications receiving 665 citations. Previous affiliations of Girish Parmar include University College of Engineering & Indian Institutes of Technology.

Order Reduction of Linear Dynamic Systems using Stability Equation Method and GA

Abstract: of linear dynamic systems using the combined advantages of stability equation method and the error minimization by Genetic algorithm. The denominator of the reduced order model is obtained by the stability equation method and the numerator terms of the lower order transfer function are determined by minimizing the integral square error between the transient responses of original and reduced order models using Genetic algorithm. The reduction procedure is simple and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. The proposed algorithm has also been extended for the order reduction of linear multivariable systems. Two numerical examples are solved to illustrate the superiority of the algorithm over some existing ones including one example of multivariable system.

Analysis of grey wolf optimizer based fractional order PID controller in speed control of DC motor

Abstract: The present work deals with comparative and robustness analysis of grey wolf optimization (GWO) based fractional order proportional–integral derivative (FOPID) controller for speed control of DC motor. The GWO is a meta-heuristic algorithm inspired from the social hunting behaviour of grey wolves as search agents. The GWO algorithm maintains a proper balance between exploration and exploitation processes. The integral of time multiplied absolute error (ITAE) has been taken as an objective function for obtaining the parameters of FOPID controller by GWO. Comparison of proposed GWO/FOPID approach with other existing techniques has also been shown along with GWO/PID. It has been observed that proposed approach with ITAE as an objective function gives less settling, rise times and comparable overshoot in comparison to existing approaches in the literature. The robustness analysis of GWO/FOPID approach has also been carried out with variations in the parameters of DC motor.

System reduction using eigen spectrum analysis and Padé approximation technique

Abstract: A mixed method is proposed for deriving reduced-order models of high-order linear time invariant systems using the combined advantages of eigen spectrum analysis and the Pade approximation technique. The denominator of the reduced-order model is found by eigen spectrum analysis, the dynamics of the numerator are chosen using the Pade approximation technique. This method guarantees stability of the reduced model if the original high-order system is stable. The method is illustrated by three numerical examples.

A robust image watermarking technique using 2-level discrete wavelet transform (DWT)

Abstract: The exponential growth in digital data over the internet has increased the requirement of a robust and high quality watermarking techniques. In general, the image watermarking techniques embed the binary or grayscale watermark into the cover image or into many multimedia images. In this method, variable visibility factor is used for the insertion of watermark into the low frequency component of the host image. In this paper, DWT-based image watermarking is proposed using level i.e. 2-level and also its parameters such as PSNR and NCC are compared with respect to 1-level DWT. The invisibility of watermarks generated using proposed method is depicted in the simulated results.

Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection

Abstract: This research’s genesis is in two aspects: first, a guaranteed solution for mitigating the grey wolf optimizer’s (GWO) defect and deficiencies. Second, we provide new open-minding insights and deep views about metaheuristic algorithms. The population-based GWO has been recognized as a popular option for realizing optimal solutions. Despite the popularity, the GWO has structural defects and uncertain performance and has certain limitations when dealing with complex problems such as multimodality and hybrid functions. This paper tries to overhaul the shortcomings of the original process and develops a GWO variant enhanced with a covariance matrix adaptation evolution strategy (CMAES), levy flight mechanism, and orthogonal learning (OL) strategy named GWOCMALOL. The algorithm uses the levy flight mechanism, orthogonal learning strategy, and CMAES to bring more effective exploratory inclinations. We conduct numerical experiments based on various functions in IEEE CEC2014. It is also compared with 10 other algorithms with competitive performances, 7 improved GWO variants, and 11 advanced algorithms. Moreover, for more systematic data analysis, Wilcoxon signed-rank test is used to evaluate the results further. Experimental results show that the GWOCMALOL algorithm is superior to other algorithms in terms of convergence speed and accuracy. The proposed GWO-based version is discretized into a binary tool through the transformation function. We evaluate the performance of the new feature selection method based on 24 UCI data sets.​ Experimental results show that the developed algorithm performs better than the original technique, and the defects are resolved. Besides, we could reach higher classification accuracy and fewer feature selections than other optimization algorithms. A narrative web service at http://aliasgharheidari.com will offer the required data and material about this work.

Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm

Abstract: In this paper, atom search optimization (ASO) algorithm and a novel chaotic version of it [chaotic ASO (ChASO)] are proposed to determine the optimal parameters of the fractional-order proportional+integral+derivative (FOPID) controller for dc motor speed control. The ASO algorithm is simple and easy to implement, which mathematically models and mimics the atomic motion model in nature, and is developed to address a diverse set of optimization problems. The proposed ChASO algorithm, on the other hand, is based on logistic map chaotic sequences, which makes the original algorithm be able to escape from local minima stagnation and improve its convergence rate and resulting precision. First, the proposed ChASO algorithm is applied to six unimodal and multimodal benchmark optimization problems and the results are compared with other algorithms. Second, the proposed ChASO-FOPID, ASO-FOPID, and ASO-PID controllers are compared with GWO-FOPID, GWO-PID, IWO-PID, and SFS-PID controllers using the integral of time multiplied absolute error (ITAE) objective function for a fair comparison. Comparisons were also made for the integral of time multiplied squared error (ITSE) and Zwe-Lee Gaing's (ZLG) objective function as the most commonly used objective functions in the literature. Transient response analysis, frequency response (Bode) analysis, and robustness analysis were all carried out. The simulation results are promising and validate the effectiveness of the proposed approaches. The numerical simulations of the proposed ChASO-FOPID and ASO-FOPID controllers for the dc motor speed control system demonstrated the superior performance of both the chaotic ASO and the original ASO, respectively.

Clustering Method for Reducing Order of Linear System using Pade Approximation

Abstract: A mixed method is proposed for finding stable reduced order models of single-input- single-output large-scale systems using Pade approximation and the clustering technique. The denominator polynomial of the reduced order model is determined by forming the clusters of the poles of the original system, and the coefficients of numerator polynomial are obtained by using the Pade approximation technique. This method guarantees stability of the reduced order model when the original high order system is stable. The methodology of the proposed method is illustrated with the help of examples from literature.