部分已知非线性多变量系统的神经网络自适应控制

Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206) Pub Date : 2001-09-05 DOI:10.1109/ISIC.2001.971524

S. Ge, Chao Wang, Yihua Tan

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引用次数: 10

摘要

针对一类具有未知非线性和参数不确定性的块三角形部分已知互联MIMO非线性系统，提出了一种自适应神经网络控制方案。MIMO系统由相互连接的子系统组成。系统状态的相互联系使得单独对单个子系统进行稳定性分析很难得出整个系统的稳定性。利用块三角形结构的特性，首先为每个子系统设计一个全状态反馈控制器，然后以嵌套迭代的方式得出所有状态变量的稳定性。保证了MIMO非线性系统闭环中所有信号的半全局一致最终有界性。系统的输出被证明收敛于期望轨迹的小邻域。通过合理选择设计参数，保证了闭环系统的控制性能。

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Adaptive control of partially known nonlinear multivariable systems using neural networks

An adaptive neural control scheme is proposed for a class of partially known interconnected MIMO nonlinear systems in block-triangular form, with both unknown nonlinearities and parametric uncertainties. The MIMO systems is composed of interconnected subsystems. The system state interconnections make it difficult to conclude the stability of the whole system by stability analysis of individual subsystem separately. By exploiting the block-triangular structure properties, we first design for each subsystem a full state feedback controller, and then conclude the stability of all the state variables in a nested iterative manner. Semi-global uniform ultimate boundedness of all the signals in the closed-loop of MIMO nonlinear systems is guaranteed. The outputs of the systems are proven to converge to small neighborhoods of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.

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Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC '01) (Cat. No.01CH37206)

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