Generic error compensation algorithm for managing flexibility of dynamic structures using neural fuzzy approaches

In this paper, an innovative scheme to tackle problems associated with managing flexibility of dynamic systems is reported. A number of solutions to this problem have been developed by means of using fuzzy logic, neural networks and neural fuzzy technique, respectively. In order to emulate the deviation of an end-effector caused by flexible structures, a spring mounted pen (SMP) is designed and incorporated in the research. A generic error compensation (GEC) algorithm is devised to correct the deviations. The method developed is essentially aiming to avoid using very complex sensors to monitor all the system and environment parameters. Comparing the desired pattern and the actual output pattern, the vision based intelligent control station can automatically make appropriate compensation through an online self-learning procedure. Numerous experimental results show that, applying the algorithms developed, the intelligent kernel can compensate for flexibility and produce excellent results, much better than a human operator.