Optimization of fuzzy logic controller using genetic algorithms
Sanjeev Tambe
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Abstract
Many non-linear, inherently unstable systems exist whose control using conventional methods is both difficult to design and unsatisfactory in implementation. Fuzzy Logic Controllers are a class of non-linear controllers that make use of human expert knowledge and an implicit imprecision to apply control to such systems. The performance of Fuzzy Logic Controller depends on its control rules and membership functions. Hence it is very important to adjust these parameters. The incorporation of genetic algorithm into a fuzzy design process adds an 'intelligent' dimension to the fuzzy controller enabling it to create and modify its rules. Genetic algorithms give the possibility of adjusting membership functions down to the level of individual rules. In this paper, the idea of model generation and optimization is explored. Fuzzy process models will be generated and parameters of fuzzy logic controller such as centre of membership function and weight of the rules are optimized using the power of genetic algorithms. The Inverted pendulum system is used as a test system for this approach.
Figures (11)
![The inverted pendulum control problem[8] is usually presented as a pole balancing task. The system to be controlled consists of a cart and a rigid pole hinged to the top of the cart. The cart can move left or right on a one-dimensional bounded track, whereas the pole can swing in the vertical plane determined by the track. The state of the system is defined by values of four system variables the cart position, cart velocity, pendulum angle and angular velocity of the pendulum pole, respectively. Control force is applied to the system to prevent the pole from falling while keeping the cart within the specified limits. The linearized system equations for this problem in the state space is given below](https://figures.academia-assets.com/91318291/figure_001.jpg)
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