未知时变时滞非线性参数化系统自适应迭代学习控制

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相关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.给出两个仿真例子验证了控制方法的有效性.

Adaptive iterative learning control for nonlinear parameterized-systems with unknown time-varying delays

A new adaptive iterative learning control approach is proposed 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, a novel adaptive control strategy is designed to ensure the tracking error for converging to zero in the mean-square sense on a finite time-interval. A sufficient condition for the convergence is also given by constructing a Lyapunov-Krasovskii-like composite energy function. The approach can be applied to the nonlinear systems with timevarying parameters and delay dependent uncertainties. Two simulation examples are provided to illustrate the efficacy of the proposed control algorithms.