Super-twisting sliding mode differentiation for improving PD controllers performance of second order systems

Summary The purpose of this paper is to present an extension of the generalised supertwisting algorithm (STA) to the multivariable framework. We begin by introducing an algorithm that may be deemed as a linear, quasicontinuous, or discontinuous multivariable system, depending on the functions that define them. For the class represented by such an algorithm we prove the robust, Lyapunov stability of the origin and characterise the perturbations that preserve its stability. In particular, when its vector field is discontinuous or quasicontinuous our algorithm is endowed with finite‐time stability. Due to its resemblance to the scalar case, we denote such finite‐time stable systems as generalised multivariable STA. Furthermore, the class of finite‐time stable systems comprise the currently available versions of STAs. To finalise, by means of simulation examples, we show that our proposed finite‐time stable algorithms are well suited for signals online differentiation and highlight their dynamical traits.

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“…Here we obtain Equations (17) and (18) in Section 3.2. To this end, please recall that K j , G j (t), L(t) and J(x) are diagonal matrices.…”

Section: Appendix B Derivations In Section 32mentioning

confidence: 99%

Generalised multivariable supertwisting algorithm

López‐Caamal

Moreno

2018

Intl J Robust & Nonlinear

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“…The GA codifies each cluster by using an array of integer data, having length equal to the number of data points. Each position of the array (gene) designates the cluster assigned to the point in that position [51][52][53][54].…”

Section: B Genetic Algorithm Based Clusteringmentioning

confidence: 99%

Clustering mixed data using an Artificial Bee Colony

Cabrera-Venegas*,

Chávez-Castilla

2019

IJITEE

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 Abstract: In this paper, we have proposed a clustering technique which optimizes the total compactness and separation (measured using the Silhouette index) of the clusters. The proposed algorithm uses an Artificial Bee Colony (ABC) based optimization method as the underlying optimization criterion. We used similarity based prototypes as cluster centers. The proposed clustering technique is able to suitably handle mixed and incomplete data types in such a way that the original characteristics of the data are preserved. Assignment of points to different clusters is done based on a dissimilarity function rather than the Euclidean distance. Results on real-life data sets show that the proposed technique is well-suited to detect true partitioning from data sets. Results are compared with those obtained by four existing clustering techniques, one genetic algorithm based clustering technique (AGKA), the k-Prototypes (KP) algorithm, well-known based K-means clustering technique for similarity functions (KMSF) and a newly developed algorithm with dissimilarity based clustering technique (AD2011).

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“…BECA ( BEe based Clustering Algorithm ) is a method based on ABC metaheuristics , inspired by the natural behavior of bees and proposed by Karaboga in [42]. This algorithm generates n initial clusters randomly that constitute the food sources [43][44][45][46][47][48][49][50] [24]. Finally, after a number of iterations defined a priori, the food source (grouping) that optimized the objective function is returned.…”

Section: A Submission Of the Papermentioning

confidence: 99%

PANTSA Influence in grouping Mixed and Incomplete Data

Chávez-Castilla*

2019

IJITEE

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Obtaining high quality groups and processing mixed and incomplete data (DMI) are still problems in the data clustering. Recently a method was proposed that improves the results obtained by clustering algorithms, the PAntSA; but this was only designed and tested for numerical data. For this reason, this paper analyzes the influence of applying the PAntSA in the performance of DMI restricted clustering algorithms. For this, the results of different algorithms are compared before and after applying the PAntSA. The comparisons made provide experimental evidence that the PAntSA algorithm improves the quality of the groups obtained by traditional DMI clustering methods.

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Super-twisting sliding mode differentiation for improving PD controllers performance of second order systems

Cited by 31 publications

References 24 publications

Generalised multivariable supertwisting algorithm

Generalised multivariable supertwisting algorithm

Clustering mixed data using an Artificial Bee Colony

PANTSA Influence in grouping Mixed and Incomplete Data

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