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Wavelet Neural Network based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

机译：基于小波神经网络的一类MIMO非线性系统的自适应鲁棒控制

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摘要

Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.
机译：基于小波神经网络的自适应鲁棒输出跟踪控制方法是针对本文未知非线性的一类MIMO非线性系统。 小波网络被构造为神经网络的替代方法，以近似系统类别的未知非线性。 建议的WNN自适应法用于补偿系统类的动态逆误差。 稳健的控制法旨在衰减近似误差和外部干扰的影响。 事实证明，该控制器提出可以保证闭环控制系统中的所有信号在Lyapunov的意义上均匀最终界限（UUB）。 最后，提出了模拟示例以说明所提出的方法的有效性和适用性。

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