基于预测模型的模糊参数自寻优S面控制器

Fuzzy parameter self-optimized S surface controller based on the prediction model

为了提高S面控制器对环境的适应能力,提出一种基于预测模型的模糊参数自寻优方法。采用非线性自回归滑动平均模型对潜水器动力学特征进行描述,并使用Elman神经网络进行模型辨识,从而建立了系统的预测模型。对于在线辨识需求,从样本容量和模型结构两个方面对预测模型进行了改进,改善了预测模型在时变环境下的预测能力。最后将建立的预测模型应用到基于模糊规则的参数自寻优S面控制器中,并进行了仿真实验。实验结果表明:该参数自寻优方法在S面控制器参数调整中取得较好的效果,改进后的S面控制器具有较快的控制响应速度。

In order to improve the adaptability of the S surface controller , a fuzzy parameter self-optimized method based on the prediction model was proposed .Firstly, the nonlinear auto-regressive moving average （ NARMA） mod-el was adopted to describe the dynamic characteristics of submersibles , and then the prediction model was estab-lished by identifying the NARMA model using the Elman neural network .For the requirement of the on-line identifi-cation, two improvements were made , i.e.sample size and model structure , thus the Elman network could replace its weights based samples updated with the change of environment .Finally, the prediction model was applied to the fuzzy parameter selfo-ptimized S surface controller .The simulation experiment was carried out and the expected effect was obtained with the parameter adjustments to the S surface controller using the proposed method .The im-proved S surface controller achieved faster response speed .