Expectation-Maximization Based Disturbance Identification and Velocity Tracking for Gimbal Servo Systems with Dynamic Imbalance

Dynamic rotor imbalance is widely identified as the primary source of disturbance in gimbal servo systems and a major factor in the deterioration of their velocity tracking performance. The imbalance disturbance is often not directly measurable, submerged in noise, and with unknown frequency, which makes the estimation of such disturbances a particularly challenging topic. In order to mitigate the effects of the unknown imbalance, this paper investigates the disturbance identification problem, which includes simultaneous identification and estimation of the disturbance. Exploiting the features of the expectation-maximization (EM) framework, the disturbance identification problem is separated into the E-step (state estimation) and the M-step (model identification). A novel disturbance identification observer, where the E-step and the M-step are solved iteratively to simultaneously update the value and internal parameter of the disturbance online is proposed. In contrast to existing work using EM for identification of practical systems, the proposed scheme can be implemented online via stochastic approximation. In addition, a discrete-time anti-disturbance sliding mode controller based on the disturbance estimation is designed. Simulation and experimental results verify the effectiveness of the proposed method.