A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot

arXiv cs.RO / 4/16/2026

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Key Points

The paper presents a Dynamic Growing Fuzzy-Neuro Controller (DGFNC) that combines fuzzy systems and neural networks to create an adaptive decision/control strategy for complex systems.
Instead of adding rules in an aggressive self-organizing way, DGFNC introduces new rules more conservatively and omits pruning, relying on an adaptive strategy to handle parameter variations.
Stability is guaranteed using a sliding-mode-based nonlinear controller integrated with the DGFNC framework.
The approach is applied and evaluated through simulations on a 3PSP parallel robot position control task, selected for its challenging dynamics.
Results from the simulations are used to argue that the method can improve response speed and reduce computation while maintaining stability.

Abstract

To date, various paradigms of soft-Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems and neural networks can make a powerful decision making system. Here, a Dynamic Growing Fuzzy Neural Controller (DGFNC) is combined with an adaptive strategy and applied to a 3PSP parallel robot position control problem. Specifically, the dynamic growing mechanism is considered in more detail. In contrast to other self-organizing methods, DGFNC adds new rules more conservatively; hence the pruning mechanism is omitted. Instead, the adaptive strategy 'adapts' the control system to parameter variation. Furthermore, a sliding mode-based nonlinear controller ensures system stability. The resulting general control strategy aims to achieve faster response with less computation while maintaining overall stability. Finally, the 3PSP is chosen due to its complex dynamics and the utility of such approaches in modern industrial systems. Several simulations support the merits of the proposed DGFNC strategy as applied to the 3PSP robot.