Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle control of Inverted Pendulum (IP) Systems

Abstract

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

An Efficient Approach in Analysis of DNA Base Calling Using Neural Fuzzy Model

“…Fuzzy logic inferencing could be implemented in production networks by manually setting the offsets. This procedure, however, receives criticism, since there is a feeling that neural networks should include training [ 2 , 3 ]. The combining of neural networks and fuzzy logic allows for the possibility of solving adjustment problems and the design constraints which are found in fuzzy logic [ 4 , 5 ].…”