Neural fuzzy based self-learning algorithms for handling flexibility of dynamic structures

This paper describes a novel approach to tackle problems associated with handling flexibility of dynamic structures. A number of solutions to this problem have been developed by innovative combination of fuzzy logic and neural networks-neural fuzzy technique. In order to emulate the deviation of an end-effector caused by flexibility, a spring mounted pen (SMP) is designed and used in the experiments. The piecewise error compensation algorithm (PEC algorithm) and the generic error compensation algorithm (GEC algorithm) are devised to correct the deviations. Comparing the desired pattern and the actual output pattern, the vision-based intelligent controller can automatically make appropriate compensation through an online self-learning process. Various experimental results indicate that applying the algorithms developed the intelligent kernel can compensate for flexibility and produce good results