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Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study.

Hashim HA, Abido MA - Comput Intell Neurosci (2015)

Bottom Line: These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE).The most effective technique in terms of system response due to different disturbances has been investigated.In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

View Article: PubMed Central - PubMed

Affiliation: System Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

ABSTRACT
This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

Show MeSH
Fitness minimization for DE with different initializations.
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Related In: Results  -  Collection


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fig13: Fitness minimization for DE with different initializations.

Mentions: Figures 10–13 present the fitness reduction for GSA, PSO, ABC, and DE, respectively, in 80 iterations. With different initial populations, GSA has been simulated in eight experiments while PSO, ABC, and DE have been simulated in five experiments in order to validate the robustness of the four search techniques.


Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study.

Hashim HA, Abido MA - Comput Intell Neurosci (2015)

Fitness minimization for DE with different initializations.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4417586&req=5

fig13: Fitness minimization for DE with different initializations.
Mentions: Figures 10–13 present the fitness reduction for GSA, PSO, ABC, and DE, respectively, in 80 iterations. With different initial populations, GSA has been simulated in eight experiments while PSO, ABC, and DE have been simulated in five experiments in order to validate the robustness of the four search techniques.

Bottom Line: These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE).The most effective technique in terms of system response due to different disturbances has been investigated.In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

View Article: PubMed Central - PubMed

Affiliation: System Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

ABSTRACT
This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

Show MeSH