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
A new architecture of wavelet neural network with multi-input-layer is proposed and implemented for modeling a class of large-scale industrial processes. Because the processes are very complicated and the number of te...A new architecture of wavelet neural network with multi-input-layer is proposed and implemented for modeling a class of large-scale industrial processes. Because the processes are very complicated and the number of technological parameters, which determine the final product quality, is quite large, and these parameters do not make actions at the same time but work in different procedures, the conventional feed-forward neural networks cannot model this set of problems efficiently. The network presented in this paper has several input-layers according to the sequence of work procedure in large-scale industrial production processes. The performance of such networks is analyzed and the network is applied to model the steel plate quality of continuous casting furnace and hot rolling mill. Simulation results indicate that the developed methodology is competent and has well prospects to this set of problems.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.
文摘A new architecture of wavelet neural network with multi-input-layer is proposed and implemented for modeling a class of large-scale industrial processes. Because the processes are very complicated and the number of technological parameters, which determine the final product quality, is quite large, and these parameters do not make actions at the same time but work in different procedures, the conventional feed-forward neural networks cannot model this set of problems efficiently. The network presented in this paper has several input-layers according to the sequence of work procedure in large-scale industrial production processes. The performance of such networks is analyzed and the network is applied to model the steel plate quality of continuous casting furnace and hot rolling mill. Simulation results indicate that the developed methodology is competent and has well prospects to this set of problems.
