Disturbance modeling compensation predictive LADRC of tank gun control system using self-attention mechanism based...

In order to address the tracking accuracy degradation of the tank gun control system (TGCS) with inherent structural nonlinearity and feedback hysteresis under complex disturbances, a Disturbance Modeling Compensated Linear Active Disturbance Rejection Predictive Control (DMC-LADRPC) method is proposed. Firstly, the mathematical model of the TGCS transmission mechanism is derived, from which the disturbance modeling compensation (DMC) term is inversely deduced and incorporated as an improvement module of LADRC. Adjusted by a gun tracking error related threshold function, the DMC term compensates the controller's output quantity to directly enhance disturbance rejection performance. Furthermore, a synergistic network consisting of a prediction module and an optimization module is introduced within the LADRC framework. The prediction module combines a bidirectional long short-term memory (Bi-LSTM) network with a multi-head self-attention mechanism to predict short-term gun future servo instructions or motion trajectories based on historical data. The optimization module adopts an Actor-Critic framework with a reward function designed according to gun tracking error and its pitch rate, enabling the controller to dynamically select the optimal pre-control instruction from the predicted sequences through interactive iteration with the TGCS to achieve minimized tracking error and prevent vibration of the servo system induced by instruction oscillations, thereby effectively compensating for feedback loop hysteresis. The results of the multi-body dynamics co-simulations and experiments demonstrate that, compared with several high-performance controllers in this field, the proposed method significantly improves the response speed and reduces the tracking error of the TGCS under various typical working conditions.