Quantum Fuzzy Controller for Inverted Pendulum System Based on Quantum Genetic Optimization

In this paper, we propose a new generalized design methodology of intelligent robust fuzzy control systems based on quantum genetic algorithm (QGA) called quantum fuzzy controller that enhance robustness of fuzzy logic controllers. The QGA is adopted because of their capabilities of directed random...

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Bibliographic Details
Published in:International journal of advanced research in computer science 2012-11, Vol.3 (7)
Main Authors: Sarker, Bishnu, Urmi, Monalisa Chowdhury, Shill, Pintu Chandra, Murase, Kazuyuki
Format: Article
Language:English
Subjects:
Computer science
Controllers
Design
Design engineering
Fuzzy logic
Fuzzy sets
Genetic algorithms
Learning
Optimization
Quantum computing
Systems design
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Summary:In this paper, we propose a new generalized design methodology of intelligent robust fuzzy control systems based on quantum genetic algorithm (QGA) called quantum fuzzy controller that enhance robustness of fuzzy logic controllers. The QGA is adopted because of their capabilities of directed random search for global optimization to find the parameters of the shape and width of membership functions and rule set of the FLC to obtain the optimal fuzzy controller simultaneously. We test the optimal FLC with modified height defuzzification as a defuzzifier obtained by the quantum computing applied on the control of dynamic balance and motion of cart-pole balancing system. We also present the conventional proportional integral derivative (PID) controller for controlling the linear system of inverted pendulum and determine which control strategy deliver better performance with respect to pendulums angle and carts position. We compare the proposed technique with existing mamdani fuzzy controller which is designed through conventional genetic algorithm and PID controller. Simulation results reveal that QGA based controller performs better than PID controller and conventional GA based controller in terms of running speed and optimizing capability.
ISSN:0976-5697