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为提高传统逆系统方法的跟踪精度和抗干扰能力,提出了基于连续分片线性神经网络α阶逆系统方法的非线性内模控制方法。利用标准连续分片神经网络逼近非线性系统的α阶逆模型,将它串连在原系统之前,得到复合的伪线性系统,对该伪线性系统应用内模控制策略进行控制,并分析了闭环系统的性能。仿真结果表明:该方法跟踪效果好、抑制干扰能力强,且设计简单,是解决非线性系统控制的一种可行的方法。
In order to improve the tracking accuracy and anti-interference ability of the traditional inverse system method, a nonlinear internal model control method based on continuous-piecewise linear neural network α-order inverse system method is proposed. The standard continuous-piece-wise neural network is used to approximate the α-order inverse model of a nonlinear system, and it is connected in series to the original system to obtain a composite pseudo-linear system. The internal-model control strategy is applied to the pseudo-linear system. Performance. The simulation results show that this method has good tracking performance, strong suppression of interference, and simple design, which is a feasible method to solve the nonlinear system control.