Decomposition strategy-based hierarchical least mean square algorithm for control systems from the impulse responses

In this research, the issue of parameter estimation for control systems is considered to develop a highly efficient estimation approach for the purpose of satisfying the need of industrial process modelling. For dynamical production processes, an error objective function in accordance with the dynamically sampled data is constructed for on-line identification. In order to simulate the instantaneous response of dynamical processes, the experimental scheme of impulse responses is adopted, and the observational data of impulse responses are used as the identification experimental data. In order to acquire high accuracy and stable performance, a hierarchical least mean square method is designed by means of the decomposition technique and the hierarchical principle. Finally, the superiority of the hierarchical least mean square approach is verified by the comparison simulation experiment and the effectiveness of the hierarchical least mean square method is proved by the detailed numerical examples.

Automated summary

The research examines parameter estimation for control systems to develop an efficient approach for industrial process modelling, constructing an error objective function and using impulse responses for online identification of dynamical production processes. The hierarchical least mean square method, designed using decomposition and hierarchical principles, achieves high accuracy and stable performance in comparison simulation experiments and numerical examples.