Fuzzy sliding-mode control for ball and beam system with fuzzy ant colony optimization

Chang, Yeong‐Hwa; Chang, Chia-Wen; Tao, Chin‐Wang; Lin, Hung-Wei; Taur, J.S.

doi:10.1016/j.eswa.2011.09.052

Cited by 69 publications

(26 citation statements)

References 27 publications

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“…Researchers applied sliding-mode control as the method with proper performance due to its fundamental property of robustness and invariance to plant model uncertainties and external disturbance which are unavoidable [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Optimal robust decoupled sliding mode control based on a Multi-objective Genetic Algorithm

2013

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Optimal design of the decoupled sliding mode control (SMC) is a great and worthwhile challenge by regarding appropriate design parameters and objective function. In the present study, Genetic Algorithm (GA) is employed as an effectual smart evolutionary algorithm to design optimally the control coefficients of an Inverted Pendulum (IP) and to eliminate tedious and repetitive trial-and-error process. When decoupled SMC is used to address the problem, there has to be a trade-off between the error of the position and the error of the angle. Hence, a Multi-objective Genetic algorithm is used to design optimally the Pareto front of the problem by regarding the errors as objective functions. Moreover, simulation has been done by considering the case with external disturbance and without external disturbance. Then, the results implemented in the MATLAB software environment are contrasted with the experimental results. Numerical results and comparison analysis elucidates the superiority of the optimal robust proposed controller over the traditional decoupled sliding mode controller in terms of attenuating the chattering and actuator variations.

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Section: Introductionmentioning

confidence: 99%

Optimal robust decoupled sliding mode control based on a Multi-objective Genetic Algorithm

2013

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“…In [18] and [19], authors presented ant fuzzy based fuzzy approaches for a Clusterization problem. In [20], authors controlled a ball and beam system with a fuzzy ant approach, where they controlled the level of pheromone update using a fuzzy logic system. The big difference between this work and ours consists essentially in applying multi-objective particle swarm optimization hydride with fuzzy ant colony optimization.…”

Section: Introductionmentioning

confidence: 99%

The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization

Elloumi

Baklouti

Abraham

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOPSO-FACO). This hybridization solves the multi-objective problem, which relies on both time performance criteria and the shortest path. Experimental results illustrate that the proposed method is efficient.

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“…The ACO is a meta-heuristic optimization method motivated by the genuine ants capacity to set up the most limited ways to their home from a sustenance source [21]. The movement of the ant depends on the quality of the pheromone trails on the ground.…”

Section: Anfaco Based Gain Parameter Optimizationmentioning

confidence: 99%

Torque Ripple Minimization in Switch Reluctance Motor Drives: an ANFACO Based Control

Chaple¹,

Bodkhe²,

Daigavane³

2018

IJIES

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Abstract:In this paper, the torque ripples in the switched reluctance motor (SRM) is constrained by utilizing an Ant Colony Optimization and an Adaptive Neuro Fuzzy Inference System (ANFACO). The control scheme is utilized for the simultaneous control of the speed and current of the SRM which fuses two controlling loops of PI controller. The external loop is governed for speed control and the internal loops is governed for current control despite the ideal decision of the turn on and turn off switching angles. With respect to, for instance, speed, current, inductance and torque, the dynamic behavior of the SRM is examined to control the speed and current which restrict the torque ripple. The gain parameters of the PI controller are ideally made as the dataset by the ant colony optimization (ACO) algorithm. The created dataset is passed to the adaptive neuro fuzzy inference system (ANFIS) for the error prediction. By then, the proposed framework is executed in the MATLAB/Simulink working stage. The execution of the proposed method is affirmed by differentiating and the current systems like ant lion optimization (ALO), non dominating sorting search algorithm (NSGA-II) and combined moth flame optimization and genetic algorithm with recurrent neural network (CMFG-RNN) procedures.

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Fuzzy sliding-mode control for ball and beam system with fuzzy ant colony optimization

Cited by 69 publications

References 27 publications

Optimal robust decoupled sliding mode control based on a Multi-objective Genetic Algorithm

Optimal robust decoupled sliding mode control based on a Multi-objective Genetic Algorithm

The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization

Torque Ripple Minimization in Switch Reluctance Motor Drives: an ANFACO Based Control

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