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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.
For a class of univariate nonlinear discrete-time systems, a zero-order near-bounded multi-model neural network adaptive controller is proposed. The controller includes a nonlinear robust adaptive controller and a nonlinear neural network adaptive Controller.When the system nonlinear term is relaxed to zero order close to bounded, the two controllers are respectively used to ensure system stability and improve system performance, the system control input by the switching mechanism between the two controllers Finally, the proofs of stability and convergence are given, and the effectiveness of this controller is verified by simulation experiments.