基于数据驱动的气动柔性关节Takagi-Sugeno模糊系统建模与预测控制
Control Theory & Applications（2022）
Abstract
针对气动柔性关节动态特性复杂、难以实现高精度控制的问题,提出一种基于Takagi-Sugeno(T-S)模糊系统的预测控制方法.首先,应用MBGD-RDA算法对T-S模糊系统进行离线训练,该算法结合了机器学习中的小批量梯度下降算法、正则化、Droprule和AdaBound算法.其次,基于模糊集相似性度量方法,对训练得到的T-S模糊系统的模糊集进行剪枝,对模糊规则进行合并,简化T-S模糊系统结构.最后,设计了基于T-S模糊系统的单层神经网络预测控制器.T-S模糊系统参数和预测控制器参数均能实现在线更新,且基于李雅普诺夫理论的稳定性分析保证了系统的稳定性.仿真结果验证了基于数据驱动的T-S模糊系统的高精度预测性能,且结构简化后的T-S模糊系统在规则数减少的情况下仍能维持较高的预测精度.实际实验中,所提控制方法最大跟踪误差小于3°,而传统的模糊逻辑控制器最大跟踪误差大于5°,这表明所提控制算法显著提升了对柔性关节的跟踪控制精度.
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