Abstract

With advances in electric drive technology, electric tracked vehicles (ETVs) have emerged as a promising solution for high-mobility ground vehicles. However, under high-speed steering conditions, the equivalent motor load inertia varies significantly, introducing strong nonlinear and time-varying characteristics into the ETV that may induce lateral instability and even rollover. To address this issue, a novel augmented deep Koopman operator-based model predictive control (ADK-MPC) method is proposed. First, a high-order sliding-mode (HOSM) observer is designed to estimate the lumped load disturbances associated with the time-varying equivalent motor load inertia. Then, the estimated disturbances are introduced as an augmented state into the DK operator to construct a data-driven augmented model. The proposed model transforms the nonlinear dynamics into a lifted linear time-invariant representation in the augmented-state space while capturing the dominant nonlinear characteristics. Based on the ADK model, an ADK-MPC controller is developed to convert the nonlinear optimization problem into a quadratic programming problem, thereby improving steering stability and reducing computational complexity. Simulation results under steering conditions indicate that the proposed method achieves better yaw rate tracking and lower computational cost than nonlinear MPC. The yaw rate tracking error is reduced by 45.5%, while the average solving time is shortened by 11.7%.

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