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针对一类时变参数化非线性系统的控制问题进行深入研究,提出一种新的迭代神经网络估计器,并证明了其逼近引理,实现了对时变不确定性的逼近.在用迭代神经网络对时变不确定性进行估计的同时,以Lyapunov稳定性理论为基础,综合运用Backstepping和自适应控制技术,设计了自适应迭代学习控制器,并进行了稳定性分析,得到了稳定性定理,解决了这类时变非线性系统的控制问题.最后的仿真实验验证了所提出设计方法的正确性.
Aiming at the control problem of a class of time-varying parametric nonlinear systems, a new iterative neural network estimator is proposed, and its approximation lemma is proved and the approximation of time-varying uncertainties is achieved. Based on the Lyapunov stability theory, the neural network is used to estimate the time-varying uncertainties. At the same time, the adaptive iterative learning controller is designed by using Backstepping and adaptive control techniques. The stability analysis is carried out and the stability Theorem, which solves the control problem of this kind of time-varying nonlinear system.The simulation results show the correctness of the proposed design method.