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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
Membership fuctions of error and rate of vertical, vertical to horizontal, and horizontal to vertical fuzzy controllers.
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fig3: Membership fuctions of error and rate of vertical, vertical to horizontal, and horizontal to vertical fuzzy controllers.

Mentions: The design of the assigned decoupling PDFLC for strong coupling and high nonlinear TRMS is shown in Figures 2, 3, and 4 as a triangular membership function. Inputs for PDFLC are expressed by error and rate of the error while the output is the control signals. The linguistic variables of the two input membership functions for the four PDFLC are described as PL, P, PS, Z, NS, N, and NL. The input of PDFLC ranged from −0.5 to 0.5 for the horizontal part and from −0.6 to 0.6 for the other three PDFLCs while output of the four membership functions is PVL, PL, P, PS, Z, NS, N, NL, and NVL within range −2.5 to 2.5. The linguistic variables are stated as PVL is positive very large, PL is positive large, P is positive, PS is positive small, Z is zero, NS is negative small, N is negative, NL is negative large, and NVL is negative very large.


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

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

Membership fuctions of error and rate of vertical, vertical to horizontal, and horizontal to vertical fuzzy controllers.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Membership fuctions of error and rate of vertical, vertical to horizontal, and horizontal to vertical fuzzy controllers.
Mentions: The design of the assigned decoupling PDFLC for strong coupling and high nonlinear TRMS is shown in Figures 2, 3, and 4 as a triangular membership function. Inputs for PDFLC are expressed by error and rate of the error while the output is the control signals. The linguistic variables of the two input membership functions for the four PDFLC are described as PL, P, PS, Z, NS, N, and NL. The input of PDFLC ranged from −0.5 to 0.5 for the horizontal part and from −0.6 to 0.6 for the other three PDFLCs while output of the four membership functions is PVL, PL, P, PS, Z, NS, N, NL, and NVL within range −2.5 to 2.5. The linguistic variables are stated as PVL is positive very large, PL is positive large, P is positive, PS is positive small, Z is zero, NS is negative small, N is negative, NL is negative large, and NVL is negative very large.

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