Online tuning of fuzzy logic controller using Kalman algorithm for conical tank system
Abstract In a non-linear process like conical tank system, controlling the liquid level was carried out by proportional integral derivative (PID) controller. But then it does not provide an accurate result. So in order to obtain accurate and effective response, intelligence is added into the system by using fuzzy logic controller (FLC). FLC which helps in maintaining the liquid level in a conical tank has been developed and applied to various fields. The result acquired using FLC will be more precise when compared to PID controller. But FLC cannot adapt a wide range of working environments and also there is no systematic method to design the membership functions (MFs) for inputs and outputs of a fuzzy system. So an adaptive algorithm called Kalman algorithm which employs fuzzy logic rules is used to adapt the Kalman filter to accommodate changes in the system parameters. The Kalman algorithm which employs fuzzy logic rules adjust the controller parameters automatically during the operation process of a system and controller is used to reduce the error in noisy environments. This technique is applied in a conical tank system. Simulations and results show that this method is effective for using fuzzy controller.
| Main Authors: | , |
|---|---|
| Format: | Digital revista |
| Language: | English |
| Published: |
Universidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y Tecnología
2017
|
| Online Access: | http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-64232017000500492 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract In a non-linear process like conical tank system, controlling the liquid level was carried out by proportional integral derivative (PID) controller. But then it does not provide an accurate result. So in order to obtain accurate and effective response, intelligence is added into the system by using fuzzy logic controller (FLC). FLC which helps in maintaining the liquid level in a conical tank has been developed and applied to various fields. The result acquired using FLC will be more precise when compared to PID controller. But FLC cannot adapt a wide range of working environments and also there is no systematic method to design the membership functions (MFs) for inputs and outputs of a fuzzy system. So an adaptive algorithm called Kalman algorithm which employs fuzzy logic rules is used to adapt the Kalman filter to accommodate changes in the system parameters. The Kalman algorithm which employs fuzzy logic rules adjust the controller parameters automatically during the operation process of a system and controller is used to reduce the error in noisy environments. This technique is applied in a conical tank system. Simulations and results show that this method is effective for using fuzzy controller. |
|---|