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Development of a GA-fuzzy-immune PID controller with incomplete derivation for robot dexterous hand.

Liu XH, Chen XH, Zheng XH, Li SP, Wang ZB - ScientificWorldJournal (2014)

Bottom Line: The control system of a robot dexterous hand and mathematical model of an index finger were presented.Moreover, immune mechanism was applied to the controller design and an improved approach through integration of GA and fuzzy inference was proposed to realize parameters' optimization.Finally, a simulation example was provided and the designed controller was proved ideal.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China ; Xuyi Mine Equipment and Materials R&D Center, China University of Mining and Technology, Huai'an 211700, China.

ABSTRACT
In order to improve the performance of robot dexterous hand, a controller based on GA-fuzzy-immune PID was designed. The control system of a robot dexterous hand and mathematical model of an index finger were presented. Moreover, immune mechanism was applied to the controller design and an improved approach through integration of GA and fuzzy inference was proposed to realize parameters' optimization. Finally, a simulation example was provided and the designed controller was proved ideal.

Show MeSH
The framework of GA-fuzzy-immune PID position controller with incomplete derivation.
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fig4: The framework of GA-fuzzy-immune PID position controller with incomplete derivation.

Mentions: The performance of improved PID controller largely depends on Kj (j = 1,2, 3), ηj (j = 1,2, 3), and f(∗). As can be seen from the above formulas, namely, (15), (16), (17), and (18), because of the nonlinear characteristics of function f(∗), a fuzzy inference algorithm is used to optimize the function f(∗). Because of the difficulty to obtain Kj (j = 1,2, 3) and ηj (j = 1,2, 3) based on analysis method, an improved genetic algorithm is proposed to solve this problem. The framework of GA-fuzzy-immune PID position controller with incomplete derivation can be built up as shown in Figure 4.


Development of a GA-fuzzy-immune PID controller with incomplete derivation for robot dexterous hand.

Liu XH, Chen XH, Zheng XH, Li SP, Wang ZB - ScientificWorldJournal (2014)

The framework of GA-fuzzy-immune PID position controller with incomplete derivation.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4109120&req=5

fig4: The framework of GA-fuzzy-immune PID position controller with incomplete derivation.
Mentions: The performance of improved PID controller largely depends on Kj (j = 1,2, 3), ηj (j = 1,2, 3), and f(∗). As can be seen from the above formulas, namely, (15), (16), (17), and (18), because of the nonlinear characteristics of function f(∗), a fuzzy inference algorithm is used to optimize the function f(∗). Because of the difficulty to obtain Kj (j = 1,2, 3) and ηj (j = 1,2, 3) based on analysis method, an improved genetic algorithm is proposed to solve this problem. The framework of GA-fuzzy-immune PID position controller with incomplete derivation can be built up as shown in Figure 4.

Bottom Line: The control system of a robot dexterous hand and mathematical model of an index finger were presented.Moreover, immune mechanism was applied to the controller design and an improved approach through integration of GA and fuzzy inference was proposed to realize parameters' optimization.Finally, a simulation example was provided and the designed controller was proved ideal.

View Article: PubMed Central - PubMed

Affiliation: School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China ; Xuyi Mine Equipment and Materials R&D Center, China University of Mining and Technology, Huai'an 211700, China.

ABSTRACT
In order to improve the performance of robot dexterous hand, a controller based on GA-fuzzy-immune PID was designed. The control system of a robot dexterous hand and mathematical model of an index finger were presented. Moreover, immune mechanism was applied to the controller design and an improved approach through integration of GA and fuzzy inference was proposed to realize parameters' optimization. Finally, a simulation example was provided and the designed controller was proved ideal.

Show MeSH