Slip-aware Adaptive Trajectory Tracking Control Strategy for Autonomous Tracked Vehicle

Due to the variable working environment and complex track-ground contact mechanism, it is difficult to establish an accurate dynamic model for tracked vehicles. Moreover, affected by the drastic impulse from the unstructured road surface, the accurate information of velocity is usually difficult or costly to measure. These unfavorable factors bring challenges to the trajectory tracking control of tracked vehicles. Aiming at the difficulty in modeling dynamics, a hybrid kino-dynamic model with track rotation acceleration as virtual control input is established, and generalized disturbances are used to describe the uncertainty caused by track slip. To deal with the unmeasurable velocity information, an extended state observer (ESO) is designed based on the hybrid model, and the state estimation of the whole vehicle is realized using only GNSS signals and track encoder signals. Finally, taking the rotational acceleration of the track as the intermediate control input, a hierarchical disturbance-rejecting control strategy consisting of an upper layer path tracking controller and a lower layer track speed controller is designed. Simulation and test results show that the proposed state observation and control strategy can accurately estimate the real-time velocity of the tracked vehicle, and effectively improve the path tracking accuracy under external disturbances. © 2024 Chinese Mechanical Engineering Society. All rights reserved.