Conventional Fuzzy Logic Controller for Balancing Two-Wheel Inverted Pendulum

Abd-elaziz, Doaa M.

doi:10.21608/jaet.2020.73045

Cited by 3 publications

(2 citation statements)

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“…In [6], in order to reduce the difficulty in the selection of the quantization factor and the proportion factor in the general fuzzy controller, a controller with the novel algorithm based on the Logistic chaotic variable is proposed. In [7], presents the design and the Real-Time implementation of self-balancing TwoWheeled Inverted Pendulum (TWIP) using state-feedback controller and a conventional fuzzy logic controller (CFLC) on Real-Time. The state-feedback controller consists of two parts PD controller and PI controller.…”

Section: Related Workmentioning

confidence: 99%

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Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

Alimoradpour

Rafie

Ahmadzadeh

2021

Preprint

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One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

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Section: Related Workmentioning

confidence: 99%

“…Based on the parameters in Table 3, the optimization output is shown in Figs. (7) to (10). The output of Fig.…”

Section: Investigation Of Optimization Stage and Parameters Of Genetic Algorithmmentioning

confidence: 99%