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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.

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TRMS setup.
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fig1: TRMS setup.

Mentions: Twin rotor is a laboratory setup for stimulating helicopter in terms of high nonlinear dynamics with strong coupling between two rotors and training various control algorithms for angle orientations. The full description of TRMS has been detailed in [1], where the system has six states defined as x = [x1, x2, x3, x4, x5, x6]T, two control signals u1 and u2, and finally the output represented by y = [x1, x3]T. The main structure of TRMS studied in this work is shown in Figure 1.


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

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

TRMS setup.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: TRMS setup.
Mentions: Twin rotor is a laboratory setup for stimulating helicopter in terms of high nonlinear dynamics with strong coupling between two rotors and training various control algorithms for angle orientations. The full description of TRMS has been detailed in [1], where the system has six states defined as x = [x1, x2, x3, x4, x5, x6]T, two control signals u1 and u2, and finally the output represented by y = [x1, x3]T. The main structure of TRMS studied in this work is shown in Figure 1.

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