非线性参数化时变时滞系统自适应迭代学习控制

Adaptive Iterative Learning Control for Nonlinearly Parameterized Systems with Time-Varying Delays

针对含有未知时变参数和时变时滞的非线性参数化系统,提出了一种新的自适应迭代学习控制方法.该方法将参数分离技术与信号置换思想相结合,可以处理含有时变参数和时滞不确定性的非线性系统.设计了一种自适应控制策略,使跟踪误差的平方在一个有限区间上的积分渐近收敛于零.通过构造Lyapunov-Krasovskii型复合能量函数,给出了闭环系统收敛的一个充分条件.给出仿真例子验证了控制方法的有效性.

A new adaptive iterative learning control approach is proposed in this paper for a class of nonlinear systems with unknown time-varying parameters and time-varying delays. By using the parameter separation technique combined with the signal replacement mechanism, the approach can be applied to nonlinear systems with time-varying parameters and delay uncertainty. A novel adaptive control strategy is designed to ensure the tracking error converging to zero in the mean-square sense on the finite interval. A sufficient condition for the conver- gence is also given by constructing a Lyapunov-Krasovskii-like composite energy function. A simulation example is provided to illustrate the effectiveness of control algorithms proposed in this paper.