动态生长模糊神经控制器提升3PSP机器人控制精度与稳定性。
A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot
- 采用动态生长机制构建模糊神经控制器，不依赖规则剪枝。
- 结合自适应策略应对参数变化，实现快速响应与低计算开销。
- 适用于复杂动力学的工业机器人控制，仿真验证有效。
目前，多种软计算范式被用于解决现代问题。其中，模糊系统与神经网络的自组织结合可形成强大的决策系统。本文将动态生长模糊神经控制器（DGFNC）与自适应策略结合，应用于3PSP并联机器人位置控制问题。特别地，详细研究了动态生长机制：相比其他自组织方法，DGFNC更保守地增加新规则，因此无需剪枝机制；取而代之的是，自适应策略使控制系统能适应参数变化。此外，基于滑模的非线性控制器确保系统稳定性。所提出的通用控制策略旨在实现更快响应、更低计算量的同时保持整体稳定。选择3PSP机器人因其复杂动力学特性，且该方法在现代工业系统中具有应用价值。多个仿真实验支持所提DGFNC策略的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。