Neuro-Fuzzy Modelling and Control of Robot Manipulators for Trajectory Tracking
Abstract
This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required modelling rules from input/output measurements recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the on-line parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as feedback servo-controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional-PID individual joint controller.
