DNN Koopmanに基づくUGV経路追従制御の偏差補償：斜面とポットホール道路の複合環境向け
DNN Koopman-Based Deviation Compensation for UGV Path Tracking Control on Coupled Slope and Potholed Road
不整地でのUGV経路追従精度向上のため、DNN Koopman理論を用いた偏差補償手法を提案し、斜面とポットホール道路での実験で有効性を検証した。
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著者: Jian Zhao, Wenbo Zhou, Zhicheng Chen, Bing Zhu, Jiayi Han, Dongjian Song, Yinju Lin, Peixing Zhang
分類: cs.RO
原文アブストラクト
Unmanned ground vehicles (UGVs) operating in off-road scenarios are confronted with complex terrain disturbances that can substantially degrade path tracking performance. To address this challenge, this paper proposes a deep neural network (DNN) Koopman-based deviation compensation strategy for UGV path tracking control. Firstly, based on the vehicle dynamic function on coupled slope, an adaptive forgetting recursive least squares method with decoupled error terms is designed to estimate tire cornering stiffness. On this basis, a Laguerre model predictive control (LMPC) path tracking control strategy is designed by incorporating Laguerre functions, which can reduce computational resource usage while maintaining reliable tracking performance across different coupled slope scenarios. Then, by integrating Koopman operator theory with DNN, a DNN Koopman (DK) path deviation compensation method is proposed, which significantly improves the path tracking accuracy of UGV under potholed road disturbances. Furthermore, an event-triggered parallel cooperative (EPC) compensation mechanism that couples LMPC with DK is established based on compensation activation criteria and credibility verification. This mechanism improves path tracking accuracy on potholed road while ensuring the feasibility of overall steering command and stability of vehicle after DK compensation. Finally, a hardware-in-the-loop (HiL) experimental platform is constructed for validation. Experimental results demonstrate that the proposed UGV path tracking strategy improves tracking performance by more than 11.5% across multiple operating conditions.
