Joint Identification and Estimation of Imbalance Torque in Gimbal Servo Systems via Variational Bayes Adaptive Expectation-Maximization

As the main source of disturbances in gimbal servo systems, the dynamic rotor imbalance can induce a torque that deteriorates system performance. The imbalance disturbance is quasi-periodic with unknown frequency, phase and amplitude, and is also submerged in stochastic noises with unknown characteristics. In this brief, the online joint identification and estimation of the imbalance in gimbal servo systems is investigated. An improved online expectation-maximization (EM) framework is utilized, which consists of a particle filter to track the posterior distribution of the imbalance, variational Bayesian adaptive Kalman filters to deal with the uncertain noise statistics, and a gradient-based solver for frequency identification. Furthermore, the stochastic approximation technique is employed to facilitate online implementation. Experimental results demonstrate the effectiveness of the proposed scheme in estimating the imbalance disturbance, identifying the unknown frequency, and adapting to uncertain noise statistics.