Convergence analysis in sense of Lebesgue-p norm of decentralized non-repetitive iterative learning control for linear large-scale systems

In this paper, a decentralized iterative learning control strategy is embedded into the procedure of hierarchical steady-state optimization for a class of linear large-scale industrial processes which consists of a number of subsystems. The task of the learning controller for each subsystem is to iteratively generate a sequence of upgraded control inputs to take responsibilities of a sequential step functional control signals with distinct scales which are determined by the local decision-making units in the two-layer hierarchical steady-state optimization processing. The objective of the designated strategy is to consecutively improve the transient performance of the system. By means of the generalized Young inequality of convolution integral, the convergence of the learning algorithm is analyzed in the sense of Lebesgue-p norm. It is shown that the inherent feature of system such as the multi-dimensionality and the interaction may influence the convergence of the non-repetitive learning rule. Numerical simulations illustrate the effectiveness of the proposed control scheme and the validity of the conclusion. This research was supported by the National Natural Science Foundation of China under Grant No. F030101–60574021.