Adaptive Neural-Network Based Fuzzy Logic (ANFIS) Based Trajectory Controller Design for One Leg of a Quadruped Robot

摘要

In this paper, a hybrid learning algorithm referred to as Adaptive Neuro Fuzzy Inference System (ANFIS) is used to obtain a neural-network based fuzzy logic (NNFL) controller to ensure walking in desired trajectory of the one leg of a quadruped robot. Firstly, Computer aided model drawing (CAD) model of system is converted into the Simulink/SimMechanics and PID controllers applied to the system Then, input and output data are obtained from PID controller set up training and checking data sets of the ANFIS. After trained network in the MATLAB/Fuzzy Logic Toolbox, NNFL controllers is acquired and applied to the system. PID controls and NNFL controllers are simulated in the MATLAB/Simulink and compared with each other according their performances in the trajectory tracking. The Simulation results are presented in graphical form to investigate the controllers.