In today's underwater environment, complex missions, such as underwater repair and docking operations, require precise control to maneuver unmanned underwater vehicles (UUVs) in extremely demanding operating conditions. Although numerous control methodologies have been used for UUVs, adaptive control is considered a promising solution due to its inherent ability to adapt to uncertainty and parameter variations. Nevertheless, it is handicapped by the tradeoff between low adaptive gains and tracking performance. Low gains are preferred to maintain stability and obtain smooth control signals. However, the resulting tracking performance, especially during transients operations, does not allow for precise maneuvering. A possible solution is model reference adaptive control (MRAC) with composite adaptation modification, which uses a prediction error in addition to the tracking error to improve learning without increasing the adaptive gains. Even though this is not a new modification to adaptive control, there is little evidence in the public domain of extensive experimental validations and quantitative analysis under low adaptive gains, especially for underwater operations. Furthermore, newer versions, such as composite MRAC (CMRAC) and predictor-based MRAC (PMRAC), offer several additional advantages. In previous publications, the authors have verified CMRAC and PMRAC for UUVs through computer simulations. Thus, this paper focuses on the experimental validation of CMRAC and PMRAC fitted to a UUV, comparing their performance under normal operations, partial thruster failure, and external disturbances. The results indicate that, while both CMRAC and PMRAC show improvements over MRAC, PMRAC has a substantial advantage over CMRAC and is recommended for future UUV applications.

中文翻译：

---

两种用于 UUV 操作的复合 MRAC 方法的实验比较，具有低适应增益

在当今的水下环境中，复杂的任务，例如水下维修和对接作业，需要精确控制以在极其苛刻的操作条件下操纵无人水下航行器 (UUV)。尽管 UUV 已经使用了多种控制方法，但自适应控制被认为是一种很有前途的解决方案，因为它具有适应不确定性和参数变化的固有能力。然而，它受到低自适应增益和跟踪性能之间的权衡的阻碍。优选低增益以保持稳定性并获得平滑的控制信号。然而，由此产生的跟踪性能，尤其是在瞬态操作期间，不允许精确操纵。一种可能的解决方案是具有复合自适应修改的模型参考自适应控制 (MRAC)，除了跟踪误差之外，它还使用预测误差来改善学习，而不会增加自适应增益。尽管这不是对自适应控制的新修改，但在公共领域几乎没有证据表明在低自适应增益下进行广泛的实验验证和定量分析，特别是对于水下操作。此外，较新的版本，例如复合 MRAC (CMRAC) 和基于预测器的 MRAC (PMRAC)，提供了一些额外的优势。在之前的出版物中，作者通过计算机模拟验证了 UUV 的 CMRAC 和 PMRAC。因此，本文重点介绍安装在 UUV 上的 CMRAC 和 PMRAC 的实验验证，比较它们在正常操作、部分推进器故障和外部干扰下的性能。结果表明，