Adaptive Steering Control Method for Heavy-Duty Electric Tracked Vehicles Based on Online Identification of Motor Load Inertia

The dual-motor drive configuration is a common transmission solution for heavy-duty electric tracked vehicles. The unclear motor load inertia makes it difficult to further improve the motor control performance, thereby limiting the driving performance of the heavy-duty electric tracked vehicle. To address this issue, this paper proposes an adaptive steering control method based on online identification of motor load inertia. Firstly, the covariance matrix updating mechanism of the least squares method is enhanced to accelerate parameter convergence, with its convergence rigorously proven via Lyapunov function analysis. This served as the foundation for establishing an online identification method. Subsequently, based on the identified variation patterns of motor load inertia, an adaptive steering control framework is established to dynamically adjust controller parameters. Simulation results demonstrate that the proposed identification method achieves a relative error below 7%. The adaptive control method significantly improves the dynamic performance of heavy-duty electric tracked vehicle steering, reducing the rise time by 5.34% at 0.2 rad/s and decreasing the overshoot by 17.19% at 0.4 rad/s.