Data-Driven MPC Scheme for Inertial Platform with Uncertain Systems Against External Vibrations

For inertial platforms with unknown model parameters and internal information, traditional model-free controllers fail to resist external vibrations solely based on the platform gyroscope, deteriorating the performance of inertial platforms. Therefore, we apply the light gradient boosting machine (LightGBM) to identify an end-to-end platform model, followed by proposing a data-driven MPC scheme to improve the control performance. Furthermore, an expectation maximization (EM) method is designed to solve the optimization problems with non-differentiable identification models, which are challenges for the traditional gradient descent-based optimizer. In addition, an adaptive compensation strategy is designed for generalizing the data-driven control scheme to different external vibrations. Finally, experimental results demonstrate the feasibility, efficacy, and generalization ability of the proposed method.