非线性零阶接近有界多模型神经网络自适应控制器

针对一类单变量非线性离散时间系统，提出一种零阶接近有界的多模型神经网络自适应控制器。该控制器包含一个非线性鲁棒自适应控制器和一个非线性神经网络自适应控制器。当系统非线性项放宽到零阶接近有界时，这两个控制器分别用于保证系统的稳定性和提高系统的性能，系统的控制输入由切换机构在两个控制器之间进行切换产生。最后给出了稳定性和收敛性证明，并通过仿真实验验证了该控制器的有效性。

Nonlinear adaptive controller using multiple models and neural networksbased on zero order proximity boundedness

A nonlinear adaptive controller using multiple models and neural network based on zero order proximity
boundedness for a class of single variable nonlinear discrete-time systems is proposed. The controller is consist of a nonlinear
robust adaptive controller and a nonlinear neural networks adaptive controller. These controllers can respectively guarantee
the stability of the system and improve the performance of the system when the restriction of nonlinear term is relaxed
to the zero order proximity boundedness. The control input can be generated by the action of the switching mechanism
between the two controllers. Finally, the stability and convergence of the system are proved, and simulation results validate
the effectiveness of proposed controllers.