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首页> 外文期刊>IEEE / ASME Transactions on Mechatronics >Multistructure Radial Basis Function Neural-Networks-Based Extended Model Predictive Control: Application to Clutch Control
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Multistructure Radial Basis Function Neural-Networks-Based Extended Model Predictive Control: Application to Clutch Control

机译：基于多结构径向基函数神经网络的扩展模型预测控制：在离合器控制中的应用

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摘要

In this article, the multistructure radial basis function neural networks (MSRBFNNs)-based extended model predictive control is further researched and discussed based on the work published in the previous conference. Taking the clutch control problem as an example, we first introduce the structure and training algorithms of the MSRBFNN in detail, and then, give a thorough derivation of the multidimensional recursive least-mean-square algorithm employed in the training process. Then, the controller design and stability analysis of the closed loop system are discussed in detail. Finally, the clutch control system model is refined, together with the simulation results and the experimental results on the test bench as verification. The results show that the proposed method is indeed effective in clutch control. As a general scheme is also proposed, it can be easily applied to other similar intelligent mechatronic systems, especially those with repeated tasks.
机译：本文在前一次会议上发表的工作的基础上，进一步研究和讨论了基于多结构径向基函数神经网络（MSRBFNN）的扩展模型预测控制。以离合器控制问题为例，首先详细介绍了MSRBFNN的结构和训练算法，然后对训练过程中采用的多维递归最小二乘算法进行了详尽的推导。然后，详细讨论了闭环系统的控制器设计和稳定性分析。最后，完善离合器控制系统模型，并结合仿真结果和试验台上的实验结果进行验证。结果表明，该方法在离合器控制中确实有效。还提出了一种通用方案，它可以轻松地应用于其他类似的智能机电系统，特别是那些重复任务的系统。

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