研究大型互联非线性分布参数系统的分散迭代学习控制问题, 该类大型互联分布参数系统由抛物型偏微分方程组或由双曲型偏微分方程组构成. 针对系统所满足的性质,基于P型学习律构建得到迭代学习控制律,在这种分散式控制方案中, 每个子系统的控制器仅依赖于该子系统的输出变量,不需要与其它子系统交换信息. 利用压缩映射原理,证明这种学习律能使得系统的输出跟踪误差于$L^2$空间内沿迭代轴方向收敛. 仿真算例说明了所得结论的可行性和有效性.

The problem of decentralized iterative learning control algorithm for a class of large-scale interconnected nonlinear distributed parameter systems is considered. Here, the considered large-scale interconnected distributed parameter systems are composed of parabolic partial differential equations or hyperbolic partial differential equations. According to system properties, iterative learning control laws are proposed for such large-scale interconnected distributed parameter systems based on P-type learning scheme. The proposed controller of each subsystem only relies on local output variables without any information exchanges with other subsystems. Using the contraction mapping method, it is shown that the scheme can guarantee the output tracking errors on $L^2$ space converge along the iteration axis. A simulation example shows the feasibility and effectiveness of the conclusion.