This article reports the development, stability analysis, and experimental evaluation of a novel adaptive identification (AID) algorithm for underwater vehicles (UVs) for on‐line estimation of plant parameters (hydrodynamic mass, quadratic drag, righting moment, and buoyancy parameters) that enter linearly into 6 degree‐of‐freedom (6‐DOF) second‐order rigid‐body UV plant dynamic models. The reported UV AID method does not require instrumentation of vehicle acceleration as is required of other standard plant parameter identification methods such as conventional least squares. All but one previously reported adaptive methods for second‐order nonlinear plants have addressed the problem of model‐based adaptive tracking control—approaches in which adaptive plant model identification is performed simultaneously with model‐based trajectory‐tracking control of fully‐actuated second‐order plants; however, these approaches are not applicable when the plant is either uncontrolled, under open‐loop control, underactuated, or using any control law other than an algorithm‐specific adaptive tracking controller. The UV AID algorithm reported herein does not require simultaneous reference trajectory‐tracking control, nor does it require instrumentation of linear acceleration or angular acceleration; thus this novel approach complements previously reported adaptive tracking methods and is applicable to a broader class of UV applications for which fully‐actuated tracking control is impractical or infeasible. We report a experimental performance analysis of the UV AID algorithm in comparison to conventional least‐square identification methods, including comparison in cross‐validation where the performance of the experimentally identified plant models obtained in identification trials are compared to experimental trials differing from the identification trials.

中文翻译：

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水下车辆全耦合二阶六自由度非线性植物模型的稳定自适应识别：理论与实验评价

本文报告了一种用于水下航行器（UV）的新型自适应识别（AID）算法的开发，稳定性分析和实验评估，该算法用于在线估算植物参数（流体动力质量，二次阻力，扶正力矩和浮力参数），该算法可以线性地输入6自由度（6-DOF）二阶刚体UV植物动力学模型。所报告的UV AID方法不需要像其他标准工厂参数识别方法（例如常规最小二乘法）那样需要车辆加速的仪表。除先前报道的用于二阶非线性植物的自适应方法外，所有方法都解决了基于模型的自适应跟踪控制的问题，在该方法中，自适应植物模型识别与基于模型的全驱动二阶轨迹跟踪控制同时进行植物; 但是，当工厂处于不受控制，处于开环控制，驱动不足或使用除算法特定的自适应跟踪控制器以外的任何控制律的情况下，这些方法将不适用。本文报道的UV AID算法不需要同时进行参考轨迹跟踪控制，也不需要线性加速度或角加速度的检测。因此，这种新颖的方法是对先前报道的自适应跟踪方法的补充，适用于无法完全实施或不可行的更广泛的UV应用程序。我们报告了与常规最小二乘识别方法相比的UV AID算法的实验性能分析，包括交叉验证中的比较，在交叉验证中，将在鉴定试验中获得的经实验鉴定的植物模型的性能与不同于鉴定试验的实验进行了比较。