论文标题
一种新的无模型方法与MIMO系统的神经网络结合
A New Model-Free Method Combined with Neural Networks for MIMO Systems
论文作者
论文摘要
在此简介中，提出了无模型的自适应预测控制（MFAPC）。它的表现优于当前的无模型自适应控制（MFAC），不仅可以解决多输入多输出（MIMO）系统中的时间延迟问题，而且还放宽了为了更大的适用范围而放松当前严格的假设。提议的控制器最有吸引力的优点是，控制器设计，性能分析和应用程序易于实现。此外，如何选择矩阵λ的问题是通过分析闭环杆的功能而不是先前的收缩映射方法来完成的。此外，鉴于神经网络（NNS）的非线性建模能力和适应性，我们将这两类算法结合在一起。所提出方法的可行性和几个有趣的结果显示在模拟中。
In this brief, a model-free adaptive predictive control (MFAPC) is proposed. It outperforms the current model-free adaptive control (MFAC) for not only solving the time delay problem in multiple-input multiple-output (MIMO) systems but also relaxing the current rigorous assumptions for sake of a wider applicable range. The most attractive merit of the proposed controller is that the controller design, performance analysis and applications are easy for engineers to realize. Furthermore, the problem of how to choose the matrix λ is finished by analyzing the function of the closed-loop poles rather than the previous contraction mapping method. Additionally, in view of the nonlinear modeling capability and adaptability of neural networks (NNs), we combine these two classes of algorithms together. The feasibility and several interesting results of the proposed method are shown in simulations.
