Neuro-fuzzy compliance control with the ability of skill acquisition from human experts

In compliant motion, the task to be performed is usually not well structured and uncertainty exists. The operational environment is either partially known or unpredictable. In applications such as the manipulation of flexible materials, the characteristics of the plant changes during operation. Conventional control methods, therefore, do not provide an appropriate solution for such problems. Intelligent control (IC), in which neural networks and fuzzy control are key components, is employed to produce a self-learning compliant motion. The neuro-fuzzy model of the compliant motion is obtained through Adaptive Spline Modelling of Observation Data (ASMOD) algorithm. This is used as the initial model of the process. An adaptive indirect fuzzy controller is then designed to control and adapt the system parameters on-line. The results are compared with previous work which employed static fuzzy control.