线性时变系统变初始条件变期望轨迹的迭代学习控制(英文)

Iterative learning control for linear time-variant continuous systems with iteration-varying initial conditions and reference trajectories

对于非严格重复线性时变连续系统,初始迭代条件和参考轨迹在一定带宽范围内都是迭代变化的.提出一种非严格的迭代学习方法来控制跟踪整流.通过该方法所获得的控制器,能保证闭环系统的所有信号是全局有界的,能够使超出初始时间间隔的输出跟踪误差收敛到一个小的残差集内,该残差集大小取决于输入矩阵的估测误差.尤其是当输入矩阵已知的情况下,能够让超出的初始时间间隔输出跟踪误差趋近于零.

For non-strictly repetitive linear time-variant continuous systems,both the iterative initial conditions and the reference trajectories are iteration-varying within a bound.We present a kind of iterative learning controller with a rectifying action to the non-strictly repetitive tracking.The proposed controller can make the output tracking error beyond the initial time interval converge to a residual set whose size depends on the estimation error of input matrix.Especially,when the accurate input matrix is known,the output tracking error beyond the initial time interval can approach zero.