Dynamic-Growing Fuzzy-Neural controller, application to a 3PSP parallel robot

Abstract

Due to the complexity of its dynamic equations, parallel robot control has become an active research area in recent years. In this application domain, model-based approaches are often time-consuming, and model-free controllers generally lack the desired performance for controlling such a fast machine. Here, a new adaptive intelligent controller is proposed for 3PSP parallel robots based on an auto-structuring fuzzy neural architecture. This control approach aims to reach high performance while maintaining overall stability. The proposed Dynamic Growing Fuzzy-Neuro Control (DGFNC) approach adds new rules more conservatively, hence pruning mechanism is omitted. Instead, an adaptive controller `adapts' the system to parameter variations. Furthermore, a sliding mode nonlinear controller ensures system stability. This hybrid approach leads to less computation and faster response. The merits of DGFNC are illustrated by simulation results as applied to the 3PSP robot.