Tuning of Fuzzy Controller by Variable Clustered Fuzzy Rules and Its Application to Overhead Crane
- Resource Type
- Conference
- Authors
- Naskar, Indrajit; Pal, A.K.; Jana, Nandan Kumar
- Source
- 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), 2023 International Conference. :119-124 Jan, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Cranes
Computational modeling
Clustering methods
Process control
Feature extraction
Stability analysis
Computational efficiency
fuzzy C-Means clustering algorithm and similarity analysis
fuzzy controller tuning
fuzzy Rule extraction
Self-tuning fuzzy proportional plus derivative controller
- Language
This paper proposed efficient rule extraction and rule reduction methods for the self-tuning fuzzy controller. The Fuzzy Clustering Method (FCM) and similarity approach are applied to extract and reduce the fuzzy gain rules. The proposed rule extraction scheme is investigated on a self-tuning fuzzy proportional plus derivative controller (STFLPDC), having 49 fuzzy rules and 49 fuzzy gain rules. The utility of the scheme is validated with various clustering validity indices. The effectiveness of the self-tuning fuzzy controller with the different reduced number of extracted fuzzy gain rules generated from clustering data is found to be quite satisfactory in comparison with the initial (49) fuzzy gain rules. The effect of gain rule variations on STFLPDC is tested to control the position and swing of an overhead crane and a comparison is made with other fuzzy and conventional controllers.