A robotic motion controller using a self-organizing fuzzy logic algorithm with grey prediction

Abstract

It is well known that robotic manipulators are highly non-linear coupled dynamic systems. It is difficult to establish an appropriate mathematical model for the design of a model-based controller. Although fuzzy logic control has model-free features, it still needs time-consuming work for rule bank and fuzzy parameter adjustment. Hence the self-organizing fuzzy controller is proposed to manipulate the motion trajectory of robots with multiple degrees of freedom. This approach has learning ability for responding to the time-varying characteristic of a robot. Its control rule bank can be established and modified continuously by on-line learning with zero initial fuzzy rules. However, this control strategy has larger oscillatory behaviour during initial learning. Here a self-organizing fuzzy controller with grey prediction is proposed to improve this behaviour. The experimental results show that this intelligent controller can reduce significantly the oscillatory amplitude of the output trajectory and tracking error.