State estimation and parameter identification method for dual-rate system based on improved Kalman prediction

For the dual-rate system, such as the process of space teleoperation whose control signals is partly determined by delayed feedback states, the state values and system parameters are coupled and influenced each other, which are hard to be estimated simultaneously. In this paper, we propose a novel method for this problem. Firstly, considering the asynchronism of the input and output sampling signals, an auxiliary model is modeled as a medium to the state and output functions. Secondly, the Kalman prediction algorithm is improved to estimate the state values at output signals of the dual-rate system. The general step is using the output estimated errors in original and auxiliary systems to modify the estimated state values of the auxiliary model, and then the unknown state values in original system is defined by the ones in auxiliary model. Based on improved Kalman algorithm and hierarchical identification algorithm, we present the detailed procedures of state estimation and parameter identification method for the dual-rate system. The processes of state estimation and parameter identification are calculated and modified alternately. Finally, the simulation results reveal that the state and parameters both approach to the real values and the state values converge faster than the parameters.

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