Optimal design of PIDA controller for induction motor using Spider Monkey Optimization algorithm

Sharma, Ajay; Sharma, Harish; Bhargava, Annapurna; Sharma, Nirmala

doi:10.1504/ijmheur.2016.081156

Cited by 14 publications

(3 citation statements)

References 23 publications

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“…Kumar et al introduced non-linear perturbation rate in SMO based on exponential function [24], chaotic function [25], and hyperopic function [26]. Major applications of SMO are engineering optimization [27], [28], antenna design [29], placement of capacitor [30], image segmentation [31], PIDA controller design [32], clustering [33] and many more. Sharma et al [34] discussed working an example of SMO.…”

Section: Related Terminologies a Spider Monkey Optimization Algorithmmentioning

confidence: 99%

Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

et al. 2021

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The epidemic increase in online reviews' growth made the sentiment classification a fascinating domain in academic and industrial research. The reviews assist several domains, which is complicated to gather annotated training data. Several sentiment classification methodologies are devised for performing the sentiment analysis, but retrieval of information is not accurately performed, less effective, and less convergence speed. In this paper, we propose a sentiment paper proposes a sentiment classification model, namely Spider Monkey Crow Optimization algorithm (SMCA), for training the deep recurrent neural network (DeepRNN). In this method, the telecom review is employed to remove stop words and stemming to eliminate inappropriate data to minimize user's seeking time. Meanwhile, the feature extraction is performed using SentiWordNet to derive the sentiments from the reviews. The extracted SentiWordNet features and other features, like elongated words, punctuation, hashtag, and numerical values, are employed in the DeepRNN for classifying sentiments. To retrieve the required review, the Fuzzy K-Nearest neighbor (Fuzzy-KNN) is employed to retrieve the review based on a distance measure. With rigorous assessments and experimentation, it is observed that the proposed SMCA-based DeepRNN performs better in terms of accuracy of 97.7%, precision of 95.5%, recall of 94.6%, and F1-score 96.7%, respectively.

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Section: Related Terminologies a Spider Monkey Optimization Algorithmmentioning

confidence: 99%

Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

et al. 2021

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“…Their results indicated that the effective hybridizations had the potential to improve the performances of both GA and SMO. As the principle of SMO is simple and the parameters in SMO are few, SMO has been applied to solve electromagnetic problems [11], economic dispatch problems [12], optimal design of PIDA controller [13] and so on. However, SMO has the same disadvantages as that in other population-based algorithms, such as low convergence accuracy and easy to fall into local optimization.…”

Section: Introductionmentioning

confidence: 99%

Modified Spider Monkey Optimization Algorithm Based on Self-Adaptive Inertia Weight

Liang¹,

Zhang²

2021

JISSR

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Spider monkey optimization (SMO) algorithm is a new swarm intelligence optimization algorithm proposed in recent years. It simulates the foraging behavior of spider monkeys which have fission-fusion social structure (FFSS). In this paper, a modified spider monkey optimization algorithm is proposed. The self-adaptive inertia weight is introduced in the local leader phase to enhance the self-learning ability of the spider monkey. According to the function value of an individual, the distance from the optimal value is determined, so the inertia weight related the individual function value is added to strength the global search ability or local search ability. The proposed algorithm is tested on 20 benchmark problems and compared with the original SMO and the hybrid algorithm SMOGA and GASMO. The numerical results show that the proposed algorithm has a certain degree of improvement in convergence accuracy and convergence speed. The performance of the proposed algorithm is also inspected by two classical engineering design problems.

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“…The PIDA controller Proposed by Jung and Dorf in 1996 an extension to the conventional PID controller which has four control parameters (proportional, integral, derivative and Acceleration control parameters) and with this new term, a closed-loop system can respond faster with less overshoot [6,7]. Similar to the PID controllers several different tuning algorithms have been proposed for the PIDA Controllers such as Flower Pollination Algorithm (FPA) [8], Genetic Algorithm (GA) [6], Bat Optimization Algorithm (BOA) [9], and Spider Monkey Optimization (SMO) algorithm [10]. The main contribution of this work is to enhance and develop the performance of the PID controller because, in some particular situations, the PID controllers are not suitable especially for higher-order control systems.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Various Intelligent Controllers’ Performance for Systems Based on Bat Optimization Algorithm

Rasheed¹

2020

ETJ

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The aim of this paper is to demonstrate the performance of two intelligent controllers; the proportional-integral-derivative (PID) controller, and the proportional-integral-derivative-acceleration (PIDA) controller, based on optimization algorithm for higher order systems. In this work, bat control algorithm has been utilized to find and tune the optimal weight parameters of the controllers as simple and fast tuning technique to find the best unsaturated state and smooth control action for the systems based on the intelligent controllers. The simulation results using (Matlab Package) show that both controllers with the bat control algorithm can give excellent performance but the performance of the PIDA controller is better than that of the PID controller in terms of reducing the rising time (Tr), peak time (Tp), settling time (Ts), maximum overshoot (Mp), and steady-state error (Ess). Furthermore, the fitness evaluation value is reduced. Keywords: PID controller, PIDA controller, Bat Optimization Algorithm, Higher-Order Systems.

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Optimal design of PIDA controller for induction motor using Spider Monkey Optimization algorithm

Cited by 14 publications

References 23 publications

Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

Modified Spider Monkey Optimization Algorithm Based on Self-Adaptive Inertia Weight

A Comparative Study of Various Intelligent Controllers’ Performance for Systems Based on Bat Optimization Algorithm

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