具有未知死区输入非线性系统的迭代学习控制

针对一类具有死区输入非线性系统,提出一种实现有限作业区间轨迹跟踪控制的神经网络迭代学习算法.基于Lyapunov-like方法设计学习控制器,回避了常规迭代学习控制中受控系统非线性特性需满足全局Lipschitz连续条件的要求.为处理输入死区,利用神经网络逼近这种强非线性特性;同时,通过对神经网络逼近误差界的估计并在控制器中设置补偿作用以消除其影响,从而提高系统的跟踪性能.

Iterative learning control of a class of nonlinear systems with unknown input dead-zone

In this paper,a neural network iterative learning controller is presented for a class of nonlinear systems with unknown input dead-zone.The learning controller is designed based on the Lyapunov-like synthesis,which can handle broader classes of system nonlinearities such as non-Lipschitz dynamics.A neural network is used as an approximator to compensate for uncertainties in the ideal controller.A tunable boundary layer is introduced to overcome the dead-zone uncertainty and through learning the undesirable ...