TY - GEN
T1 - Cascaded MPC with Kinematic-Dynamic Dual-ESO for Trajectory Tracking of Tracked Vehicles
AU - Ma, Xiaohan
AU - Yuan, Shihua
AU - Wu, Wei
AU - Wang, Jingyang
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - To address trajectory tracking deviations in tracked vehicles caused by significant unmodeled kinematic slip and dynamic terrain resistance uncertainties, this paper proposes a novel cascaded Model Predictive Control (MPC) architecture integrated with a Kinematic-Dynamic Dual Extended State Observer (Dual-ESO). Specifically, the inner-loop dynamic ESO estimates physical torque resistances, while the outer-loop kinematic ESO reconstructs time-varying geometric slips for active feedforward compensation. The proposed framework is comprehensively validated under significant compound disturbance scenarios, including continuous dynamic slips, step lateral skids, sudden yaw drifts, and abrupt terrain resistance surges. Simulation results demonstrate that the constructed Dual-ESO rapidly and accurately reconstructs the multi-source disturbances. Consequently, compared to the conventional cascaded kinematic MPC and Active Disturbance Rejection Control (ADRC) framework, the proposed architecture yields superior tracking precision, reducing both the Integral Time Absolute Error (ITAE) and the Root Mean Square Error (RMSE) along both the X and Y axes by over 73%.
AB - To address trajectory tracking deviations in tracked vehicles caused by significant unmodeled kinematic slip and dynamic terrain resistance uncertainties, this paper proposes a novel cascaded Model Predictive Control (MPC) architecture integrated with a Kinematic-Dynamic Dual Extended State Observer (Dual-ESO). Specifically, the inner-loop dynamic ESO estimates physical torque resistances, while the outer-loop kinematic ESO reconstructs time-varying geometric slips for active feedforward compensation. The proposed framework is comprehensively validated under significant compound disturbance scenarios, including continuous dynamic slips, step lateral skids, sudden yaw drifts, and abrupt terrain resistance surges. Simulation results demonstrate that the constructed Dual-ESO rapidly and accurately reconstructs the multi-source disturbances. Consequently, compared to the conventional cascaded kinematic MPC and Active Disturbance Rejection Control (ADRC) framework, the proposed architecture yields superior tracking precision, reducing both the Integral Time Absolute Error (ITAE) and the Root Mean Square Error (RMSE) along both the X and Y axes by over 73%.
KW - Cascaded MPC
KW - Dual Extended state observer
KW - Slip and skid
KW - Tracked vehicles
KW - Trajectory tracking
UR - https://www.scopus.com/pages/publications/105048787338
U2 - 10.1109/ICMA69663.2026.11647629
DO - 10.1109/ICMA69663.2026.11647629
M3 - Conference contribution
AN - SCOPUS:105048787338
T3 - 2026 IEEE International Conference on Mechatronics and Automation, ICMA 2026
SP - 1503
EP - 1508
BT - 2026 IEEE International Conference on Mechatronics and Automation, ICMA 2026
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Mechatronics and Automation, ICMA 2026
Y2 - 2 August 2026 through 5 August 2026
ER -