为微型水下无人机建立动力学模型并自适应估计控制输入
System Identification and Adaptive Input Estimation on the Jaiabot Micro Autonomous Underwater Vehicle
- 基于实测数据构建航向与纵向往复的线性动态模型
- 自适应算法能准确估计未知控制输入和系统状态
- 适合需在噪声中精准控制的水下机器人研究者
本文针对新型海洋自主水下航行器Jaiabot，尝试建模其系统动力学并估计未知内部控制输入与状态。尽管Jaiabot已在多项应用中展现潜力，但过程与传感器噪声仍需状态估计与降噪处理。本工作首次基于实地测试数据，建立了适用于Jaiabot的纵向往复与航向线性动态模型。采用自适应输入估计算法，精确估计控制输入与系统状态。通过与经典卡尔曼滤波对比验证，该方法在处理未知控制输入方面具有显著优势。
原文摘要 · Abstract (English)
This paper reports an attempt to model the system dynamics and estimate both the unknown internal control input and the state of a recently developed marine autonomous vehicle, the Jaiabot. Although the Jaiabot has shown promise in many applications, process and sensor noise necessitates state estimation and noise filtering. In this work, we present the first surge and heading linear dynamical model for Jaiabots derived from real data collected during field testing. An adaptive input estimation algorithm is implemented to accurately estimate the control input and hence the state. For validation, this approach is compared to the classical Kalman filter, highlighting its advantages in handling unknown control inputs.
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