Auxiliary model-based identification method for quantized control systems
基于辅助模型的量化控制系统辨识方法

An auxiliary model-based identification method for quantized systems subjected to communication constraints is introduced, based on the technique of repetitive stochastic empirical measurements. The model characteristics of the quantized system are analyzed, and a two-step identification strategy is presented. It is shown that the quantized system based on repetitive stochastic empirical measurements involves time-varying estimation error. The persistent exciting condition for parameter identification is derived. The auxiliary model-based quantized multi-innovation recursive algorithm for quantized systems is also given. Convergence analysis of the auxiliary model-based algorithm provides the method for computing the upper bound of parameter identification error. It is demonstrated that under certain conditions, the recursivealgorithm is consistently convergent. Finally, this identification method is extended to a class of Hammerstein nonlinear quantized systems. Simulation results show the effectiveness of the conclusions.