An attention-based deep kernel learning identification method for turntable servo systems

Accurate modeling of the turntable servo systems is crucial for high-precision motion control applications such as radar tracking and aerospace simulation. To improve modeling accuracy for nonlinear effects in turntable servo system under small-sample conditions, this paper proposes an attention-based deep kernel learning identification method. By introducing a multi-head attention mechanism into the feature extraction stage of deep kernel learning, the capability to extract nonlinear input features is strengthened, thereby improving the accuracy of nonlinear model identification. Since data on nonlinear factors in the turntable servo system are difficult to measure, an alternating compensation strategy between linear and nonlinear model errors is adopted on the basis of the attention-based deep kernel learning method. This enables the separate identification of linear and nonlinear models and ultimately improves the overall modeling accuracy of the turntable servo system. Simulation and experimental results demonstrate that, under small-sample conditions, the turntable servo system model prediction value obtained by the attention-based deep kernel learning identification method performs better than the deep neural network and deep kernel learning methods in terms of indicators such as mean squared error and quantitative fit.