In context
In early 2025, autonomous ground vehicles (UGVs) were increasingly deployed in complex outdoor environments where model uncertainties and external disturbances degrade tracking performance. Conventional linear controllers and single-loop active disturbance rejection control (ADRC) often struggled to estimate high-order disturbances accurately, limiting trajectory precision. This paper addressed that gap by introducing a cascaded observer architecture with bias correction.
What was reported
Researchers from Tiangong University and Nankai University developed a cascaded ADRC strategy with observation bias rectification (OBR-ADRC) for UGV trajectory tracking. The method reconstructs generalized external and internal disturbances as a total disturbance, then uses a primary fourth-order extended state observer (ESO1) with correction terms to expand the observation order. A secondary third-order observer (ESO2) estimates residual disturbance not captured in time, improving overall disturbance estimation accuracy.
Based on feedback linearization, a trajectory tracking control law incorporating total disturbance estimates was designed to accelerate error convergence and boost tracking precision. A parameter configuration rule maps key controller gains to the closed-loop model, enhancing adaptability across different UGV models. Lyapunov-based analysis proved asymptotic stability and defined stability boundaries.
Comparative experiments under various scenarios showed the proposed strategy significantly improves trajectory tracking in uncertain environments compared to typical linear ADRC, especially when disturbances are complex and high-order.
Why it mattered
This work offered a practical path to higher-precision UGV control without heavy model dependence, which is valuable for industrial automation scenarios like warehouse logistics and field inspection where terrain and load variations introduce unpredictable disturbances. The cascaded observer design and parameter tuning rules could be adapted to other mobile robots, potentially improving robustness in real-world deployments.
The experimental results show that the proposed strategy can effectively improve the trajectory tracking effect of UGV in uncertain environments.
Source: 《机器人》期刊 (robot.sia.cn) · Published 2025-03-15 · “基于观测偏差校正的无人地面车级联自抗扰跟踪策略”