In this paper,a decentralized iterative learning control strategy is embedded into theprocedure of hierarchical steady-state optimization for a class of linear large-scale industrial processeswhich consists of a numbe...In this paper,a decentralized iterative learning control strategy is embedded into theprocedure of hierarchical steady-state optimization for a class of linear large-scale industrial processeswhich consists of a number of subsystems.The task of the learning controller for each subsystem is toiteratively generate a sequence of upgraded control inputs to take responsibilities of a sequential stepfunctional control signals with distinct scales which are determined by the local decision-making units inthe two-layer hierarchical steady-state optimization processing.The objective of the designated strategyis to consecutively improve the transient performance of the system.By means of the generalized Younginequality of convolution integral,the convergence of the learning algorithm is analyzed in the sense ofLebesgue-p norm.It is shown that the inherent feature of system such as the multi-dimensionality andthe interaction may influence the convergence of the non-repetitive learning rule.Numerical simulationsillustrate the effectiveness of the proposed control scheme and the validity of the conclusion.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. F030101 60574021.
文摘In this paper,a decentralized iterative learning control strategy is embedded into theprocedure of hierarchical steady-state optimization for a class of linear large-scale industrial processeswhich consists of a number of subsystems.The task of the learning controller for each subsystem is toiteratively generate a sequence of upgraded control inputs to take responsibilities of a sequential stepfunctional control signals with distinct scales which are determined by the local decision-making units inthe two-layer hierarchical steady-state optimization processing.The objective of the designated strategyis to consecutively improve the transient performance of the system.By means of the generalized Younginequality of convolution integral,the convergence of the learning algorithm is analyzed in the sense ofLebesgue-p norm.It is shown that the inherent feature of system such as the multi-dimensionality andthe interaction may influence the convergence of the non-repetitive learning rule.Numerical simulationsillustrate the effectiveness of the proposed control scheme and the validity of the conclusion.
