Oil spill at the Gulf of Mexico a few years ago sets great hazardous to environments. The runtime monitoring and tracking of plume pose challenges to research community. Robot-based approaches have been proposed in previous work to solve this problem in environments with known physical parameters and measurable currents. However, practical implementation of this type of control laws may require additional sensors and the sensing error may significantly impact the convergence. This paper extends previous work by overcoming the challenge to establish a new control law by integrating interactive estimation and control in a unified loop. With a deliberate design, the parameters can be estimated online and the control can be achieved at the same time with provable convergence of the overall system, despite of the interplay of the two parts. An auxiliary system is constructed for efficient estimation of unknown parameters and set projection is incorporated to further improve the transient performance, making the system work well in both slow and fast time-varying environments. Theories of convergence, stability, and transient state are presented to guarantee the performance of plume front tracking. Validation experiments verify the theoretical results and substantiate the efficacy of the proposed scheme for plume tracking in unknown environments.

中文翻译：

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通过估计和控制在未知环境中进行羽流前沿跟踪


几年前墨西哥湾的漏油事件对环境造成了极大的危害。羽流的运行时监测和跟踪给研究界带来了挑战。之前的工作中已经提出了基于机器人的方法来解决具有已知物理参数和可测量电流的环境中的这个问题。然而，此类控制律的实际实施可能需要额外的传感器，并且传感误差可能会显着影响收敛。本文通过将交互式估计和控制集成在一个统一的循环中，克服了建立新控制律的挑战，扩展了之前的工作。通过深思熟虑的设计，可以在线估计参数，并且可以同时实现控制，同时证明整个系统的收敛性，尽管两个部分之间存在相互作用。构建了辅助系统来有效估计未知参数，并结合集合投影来进一步提高瞬态性能，使系统在慢速和快速时变环境中都能正常工作。提出了收敛、稳定性和瞬态理论来保证羽流前沿跟踪的性能。验证实验验证了理论结果并证实了所提出的未知环境中羽流跟踪方案的有效性。