A Modified Multi-innovation Algorithm to Turntable Servo System Identification

Abstract

This paper is concerned with the identification of turntable servo system through the usage of a reframed multi-innovation least-squares scheme. A Wiener–Hammerstein model is employed in this paper to depict the dynamic characteristics of the turntable system. In the test bed, the stabilized platform can be considered as a linear dynamic subsystem. The motor is also a linear dynamic subsystem. And the major nonlinearity characteristic between motor and platform is captured by a continuously differentiable friction model. A new reframed multi-innovation least-squares approach (RMILS) is proposed to identify the Wiener–Hammerstein model. By introducing the intermediary step updating, the innovation updating is decomposed into sub-innovations updating, which can solve the inverse of covariance matrix and improve the identification performance. Then, the consistency nature of the RMILS method is discussed by using the theoretical analysis. Finally, the simulation and experiment results explain that the developed approach produces an outstanding performance in convergence speed and identification precision comparing to the conventional multi-innovation least-squares approach.