New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system

Castillo, Oscar; Lizárraga, Evelia; Soria, José Manuel Nieto; Melín, Patricia; Valdez, Fevrier

doi:10.1016/j.ins.2014.09.040

Cited by 79 publications

(25 citation statements)

References 16 publications

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“…The future research will be focused on the development of neural network models and other appropriate applications with various optimization approaches [41][42][43][44][45][46][47][48][49][50] included in the identification algorithm. The development of fuzzy models for the translation from French to English will require modifications in the structure of the identification algorithms.…”

Section: Discussionmentioning

confidence: 99%

Evolving Fuzzy Models for Automated Translation

2017

APH

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Section: Discussionmentioning

confidence: 99%

Evolving Fuzzy Models for Automated Translation

2017

APH

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“…Charged System Search algorithms are applied in [9] to the optimal tuning of PI-fuzzy controllers for DC servo systems. Ant Colony Optimization is applied in [10,11] to the optimal tuning of fuzzy controllers for robots and ball and bam systems.…”

Section: Introductionmentioning

confidence: 99%

An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning

et al. 2017

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This paper proposes an easily understandable Grey Wolf Optimizer (GWO) applied to the optimal tuning of the parameters of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). GWO is employed for solving optimization problems focused on the minimization of discrete-time objective functions defined as the weighted sum of the absolute value of the control error and of the squared output sensitivity function, and the vector variable consists of the tuning parameters of the T-S PI-FCs. Since the sensitivity functions are introduced with respect to the parametric variations of the process, solving these optimization problems is important as it leads to fuzzy control systems with a reduced process parametric sensitivity obtained by a GWO-based fuzzy controller tuning approach. GWO algorithms applied with this regard are formulated in easily understandable terms for both vector and scalar operations, and discussions on stability, convergence, and parameter settings are offered. The controlled processes referred to in the course of this paper belong to a family of nonlinear servo systems, which are modeled by second order dynamics plus a saturation and dead zone static nonlinearity. Experimental results concerning the angular position control of a laboratory servo system are included for validating the proposed method.

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“…It was possible using systematic and hierarchical optimization approach modifying the conventional ACO algorithm using an ant set partition strategy [12]. The diversity control in ACO is presented using fuzzy approach.…”

Section: Introductionmentioning

confidence: 99%

“…Still the problem of selecting appropriate membership based on number of rules in rule base is not available in literature. This manuscript is to decide appropriate type of membership function with varying number of rules [12,13]. The section criterion is validated on SMIB as well as on multi-machine power system.…”

Section: Introductionmentioning

confidence: 99%