时变参数化非线性系统自适应迭代学习控制器设计

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.

Adaptive iterative learning controller design for a class of time-varying parameterized nonlinear systems

In this paper,a class of nonlinear systems with time-varying uncertainties are studied.A new iterative learning neural network approximator design scheme and corresponding boundedness lemma are proposed to approximate timevarying uncertainties.On the basis of Lyapunov stability theory,the iterative learning controller is designed by using backstepping and adaptive control technique.The stability theorem is obtained according to the stability analysis,which solves the control problem of the time-varying nonlinear system.The simulation results validate the correctness of proposed scheme.