Real-time implementation of a dynamic fuzzy neural networks controller for a SCARA

摘要

This paper presents the design, development and implementation of a Dynamic Fuzzy Neural Networks (D-FNNs) Controller suitable for real-time industrial applications. The unique feature of the D-FNNs controller is that it has dynamic self-organising structure, fast learning speed, good generalisation and flexibility in learning. The approach of rapid prototyping is employed to implement the D-FNNs controller with a view of controlling a Selectively Compliance Assembly Robot Arm (SCARA) in real time. Simulink, an iterative software for simulating dynamic systems, is used for modelling, simulation and analysis of the dynamic system. The D-FNNs controller was implemented through Real-Time Workshop (RTW). RTW generates C-codes from the Simulink block diagrams and in turn, the generated codes (object codes) are downloaded to the dSPACE DS1102 floating-point processor, together with the supporting files, for execution. The performance of the D-FNNs controller was found to be superior and it matches favourably with the simulation results.