Abstract
This paper presents a comparison study of different control strategies for stabilizing highly non- linear Gantry inverted pendulum (GIP) system. The control objective was achieved using three different soft- computing techniques i.e. Fuzzy logic (FL), Adaptive neuro fuzzy inference system (ANFIS) and Neural networks (NN's). The results obtained from fuzzy controller were further optimized using ANFIS and NN's controllers. The performance parameters considered for analysis were Settling time (seconds), Maximum Overshoot (degree) and Steady state error. The simulation results that both fuzzy and ANFIS controllers were able to stabilize the non-linear GIP system within specified time. It was also observed that ANFIS controller shows better learning ability as compared to NN's controller. The study also elaborates the relationship between Membership functions (MF's) and training error tolerance for ANFIS controller and relation between hidden neurons and Mean squared error (MSE) and Regression (R) value for NN's controller. Index Terms—Gantry inverted pendulum, Fuzzy Logic, ANFIS, NN's, Membership functions, FLC.
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