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首页> 外文会议>Systems and Control in Aerospace and Astronautics, 2008. ISSCAA 2008. >Nonlinear Adaptive Control Using Recurrent Fuzzy Models with Stability Analysis
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Nonlinear Adaptive Control Using Recurrent Fuzzy Models with Stability Analysis

机译：具有稳定性分析的递归模糊模型的非线性自适应控制。

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摘要

In this paper, an indirect adaptive control scheme based on Takagi-Sugeno(TS)-type recurrent fuzzy models is proposed for nonlinear plants with unmeasurable states. The TS-type recurrent fuzzy model is used as the dynamic model of the nonlinear plant. Its recurrent property comes from that it can memorize temporal information with the feedback connections between its states layer and inputs layer, which makes it capable of more powerful learning ability compared with ordinary TS fuzzy models. The parameters of the model are adapted on-line by using gradient based neural network learning methods to allow for partially unknown or time-varying plants. The controller is designed completely based on the model structure, parameters and states. Comprehensive convergence analysis of the proposed adaptive nonlinear control schemes is studied and stability conditions are given. The effectiveness of the proposed control scheme is finally demonstrated by simulation examples.
机译：提出了一种基于Takagi-Sugeno（TS）型递归模糊模型的间接自适应控制方案，用于状态不可测的非线性工厂。 TS型递归模糊模型被用作非线性工厂的动力学模型。它的递归特性来自于它可以通过状态层和输入层之间的反馈连接来存储时间信息，这使其与普通的TS模糊模型相比具有更强大的学习能力。通过使用基于梯度的神经网络学习方法对模型的参数进行在线调整，以允许部分未知或时变的植物。完全根据模型结构，参数和状态来设计控制器。研究了所提出的自适应非线性控制方案的综合收敛性分析，并给出了稳定性条件。仿真实例最终证明了所提出的控制方案的有效性。

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